{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The Hill-Tononi Neuron and Synapse Models\n",
    "\n",
    "## Hans Ekkehard Plesser, NMBU/FZ Jülich/U Oslo, 2016-12-01\n",
    "\n",
    "## Background\n",
    "\n",
    "This notebook describes the neuron and synapse model proposed by Hill and Tononi in *J Neurophysiol* 93:1671-1698, 2005 ([doi:10.1152/jn.00915.2004](http://dx.doi.org/doi:10.1152/jn.00915.2004)) and their implementation in NEST. The notebook also contains some tests.\n",
    "\n",
    "This description is based on the original publication and publications cited therein, an analysis of the source code of the original Synthesis implementation kindly provided by Sean Hill, and plausiblity arguments.\n",
    "\n",
    "In what follows, I will refer to the original paper as [HT05].\n",
    "\n",
    "This notebook was run successfully with NEST Branch HT_NMDA at Commit bec1c52 (15 Dec 2016)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Neuron Model\n",
    "\n",
    "### Integration \n",
    "\n",
    "The original Synthesis implementation of the model uses Runge-Kutta integration with fixed 0.25 ms step size, and integrates channels dynamics first, followed by integration of membrane potential and threshold.\n",
    "\n",
    "NEST, in contrast, integrates the complete 16-dimensional state using a single adaptive-stepsize Runge-Kutta-Fehlberg-4(5) solver from the GNU Science Library (`gsl_odeiv_step_rkf45`).\n",
    "\n",
    "### Membrane potential\n",
    "\n",
    "Membrane potential evolution is governed by [HT05, p 1677]\n",
    "\n",
    "\\begin{equation}\n",
    "\\frac{\\text{d}V}{\\text{d}t} = \\frac{-g_{\\text{NaL}}(V-E_{\\text{Na}})\n",
    "-g_{\\text{KL}}(V-E_{\\text{K}})+I_{\\text{syn}}+I_{\\text{int}}}{\\tau_{\\text{m}}}\n",
    "-\\frac{g_{\\text{spike}}(V-E_{\\text{K}})}{\\tau_{\\text{spike}}}\n",
    "\\end{equation}\n",
    "\n",
    "- The equation does not contain membrane capacitance. As a side-effect, all conductances are dimensionless.\n",
    "- Na and K leak conductances $g_{\\text{NaL}}$ and $g_{\\text{KL}}$ are constant, although $g_{\\text{KL}}$ may be adjusted on slow time scales to mimic neuromodulatory effects.\n",
    "- Reversal potentials $E_{\\text{Na}}$ and $E_{\\text{K}}$ are assumed constant.\n",
    "- Synaptic currents $I_{\\text{syn}}$ and intrinsic currents $I_{\\text{int}}$ are discussed below. In contrast to the paper, they are shown with positive sign here (just change in notation).\n",
    "- The last term is a re-polarizing current only active during the refractory period, see below. Note that it has a different (faster) time constant than the other currents. It might have been more natural to use the same time constant for all currents and instead adjust $g_{\\text{spike}}$. We follow the original approach here."
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Threshold, Spike generation and refractory effects\n",
    "\n",
    "The threshold evolves according to [HT05, p 1677]\n",
    "\n",
    "\\begin{equation}\n",
    "\\frac{\\text{d}\\theta}{\\text{d}t} = -\\frac{\\theta-\\theta_{\\text{eq}}}{\\tau_{\\theta}}\n",
    "\\end{equation}\n",
    "\n",
    "The neuron emits a single spike if \n",
    "- it is not refractory\n",
    "- membrane potential crosses the threshold, $V\\geq\\theta$\n",
    "\n",
    "Upon spike emission,\n",
    "- $V \\leftarrow E_{\\text{Na}}$\n",
    "- $\\theta \\leftarrow E_{\\text{Na}}$\n",
    "- the neuron becomes refractory for time $t_{\\text{spike}}$ (`t_ref` in NEST)\n",
    "\n",
    "The repolarizing current is active during, and only during the refractory period:\n",
    "\\begin{equation}\n",
    "g_{\\text{spike}} = \\begin{cases}  1  & \\text{neuron is refractory}\\\\\n",
    " 0 & \\text{else} \\end{cases}\n",
    "\\end{equation}\n",
    "\n",
    "During the refractory period, the neuron cannot fire new spikes, but all state variables evolve freely, nothing is clamped. \n",
    "\n",
    "The model of spiking and refractoriness is based on Synthesis model `PulseIntegrateAndFire`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Intrinsic currents\n",
    "\n",
    "Note that not all intrinsic currents are active in all populations of the network model presented in [HT05, p1678f].\n",
    "\n",
    "Intrinsic currents are based on the Hodgkin-Huxley description, i.e.,\n",
    "\n",
    "\\begin{align}\n",
    "I_X &= g_{\\text{peak}, X} m_X(V, t)^N_X h_X(V, t)(V-E_X) \\\\\n",
    "\\frac{\\text{d}m_X}{\\text{d}t} &= \\frac{m_X^{\\infty}-m_X}{\\tau_{m,X}(V)}\\\\\n",
    "\\frac{\\text{d}h_X}{\\text{d}t} &= \\frac{h_X^{\\infty}-h_X}{\\tau_{h,X}(V)}\n",
    "\\end{align}\n",
    "\n",
    "where $I_X$  is the current through channel $X$ and $m_X$ and $h_X$ the activation and inactivation  variables for channel $X$.\n",
    "\n",
    "#### Pacemaker current $I_h$\n",
    "\n",
    "Synthesis: `IhChannel`\n",
    "\n",
    "\\begin{align}\n",
    "N_h & = 1 \\\\\n",
    "m_h^{\\infty}(V) &= \\frac{1}{1+\\exp\\left(\\frac{V+75\\text{mV}}{5.5\\text{mV}}\\right)} \\\\\n",
    "\\tau_{m,h}(V) &= \\frac{1}{\\exp(-14.59-0.086V) + \\exp(-1.87  + 0.0701V)} \\\\\n",
    "h_h(V, t) &\\equiv 1 \n",
    "\\end{align}\n",
    "\n",
    "Note that subscript $h$ in some cases above marks the $I_h$ channel."
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Low-threshold calcium current $I_T$\n",
    "\n",
    "Synthesis: `ItChannel`\n",
    "\n",
    "##### Equations given in paper \n",
    "\n",
    "\\begin{align}\n",
    "N_T & \\quad \\text{not given} \\\\\n",
    "m_T^{\\infty}(V) &= 1/\\{1 +  \\exp[ -(V +  59.0)/6.2]\\} \\\\\n",
    "\\tau_{m,T}(V) &= \\{0.22/\\exp[ -(V  + 132.0)/ 16.7]\\} +  \\exp[(V  + 16.8)/18.2] +  0.13\\\\\n",
    "h_T^{\\infty}(V) &= 1/\\{1 +  \\exp[(V +  83.0)/4.0]\\} \\\\\n",
    "\\tau_{h,T}(V) &= \\langle  8.2 +  \\{56.6 +  0.27 \\exp[(V +  115.2)/5.0]\\}\\rangle / \\{1.0 +  \\exp[(V +  86.0)/3.2]\\}\n",
    "\\end{align}\n",
    "\n",
    "Note the following:\n",
    "- The channel model is based on Destexhe et al, *J Neurophysiol* 76:2049 (1996).\n",
    "- In the equation for $\\tau_{m,T}$, the second exponential term must be added to the first (in the denominator) to make dimensional sense; 0.13 and 0.22 have unit ms.\n",
    "- In the equation for $\\tau_{h,T}$, the $\\langle \\rangle$ brackets should be dropped, so that $8.2$ is not divided by the $1+\\exp$ term. Otherwise, it could have been combined with the $56.6$.\n",
    "- This analysis is confirmed by code analysis and comparison with Destexhe et al, *J Neurophysiol* 76:2049 (1996), Eq 5.\n",
    "- From Destexhe et al we also find $N_T=2$.\n",
    "\n",
    "##### Corrected equations\n",
    "\n",
    "This leads to the following equations, which are implemented in Synthesis and NEST.\n",
    "\n",
    "\\begin{align}\n",
    "N_T &= 2 \\\\\n",
    "m_T^{\\infty}(V) &=  \\frac{1}{1+\\exp\\left(-\\frac{V+59\\text{mV}}{6.2\\text{mV}}\\right)}\\\\\n",
    "\\tau_{m,T}(V) &= 0.13\\text{ms} \n",
    "  + \\frac{0.22\\text{ms}}{\\exp\\left(-\\frac{V  + 132\\text{mV}}{16.7\\text{mV}}\\right) + \\exp\\left(\\frac{V +  16.8\\text{mV}}{18.2\\text{mV}}\\right)} \\\\ \n",
    "h_T^{\\infty}(V) &=  \\frac{1}{1+\\exp\\left(\\frac{V+83\\text{mV}}{4\\text{mV}}\\right)}\\\\\n",
    "\\tau_{h,T}(V) &= 8.2\\text{ms} +  \\frac{56.6\\text{ms} +  0.27\\text{ms} \\exp\\left(\\frac{V   + 115.2\\text{mV}}{5\\text{mV}}\\right)}{1 +   \\exp\\left(\\frac{V  + 86\\text{mV}}{3.2\\text{mV}}\\right)}\n",
    "\\end{align}"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Persistent Sodium Current $I_{NaP}$\n",
    "\n",
    "Synthesis: `INaPChannel`\n",
    "\n",
    "This model has only activation ($m$) and uses the steady-state value, so the only relevant equation is that for $m$. In the paper, it is given as\n",
    "\n",
    "\\begin{equation}\n",
    "m_{NaP}^{\\infty}(V) = 1/[1+\\exp(-V+55.7)/7.7]\n",
    "\\end{equation}\n",
    "\n",
    "Dimensional analysis indicates that the division by $7.7$ should be in the argument of the exponential, and the minus sign needs to be moved so that the current activates as the neuron depolarizes leading to the corrected equation\n",
    "\n",
    "\\begin{equation}\n",
    "m_{NaP}^{\\infty}(V) = \\frac{1}{1+\\exp\\left(-\\frac{V+55.7\\text{mV}}{7.7\\text{mV}}\\right)}\n",
    "\\end{equation}\n",
    "\n",
    "This equation is implemented in NEST and Synthesis and is the one found in Compte et al (2003), cited by [HT05, p 1679].\n",
    "\n",
    "##### Corrected exponent\n",
    "\n",
    "According to Compte et al (2003), $N_{NaP}=3$, i.e.,\n",
    "\\begin{equation}\n",
    "I_{NaP} = g_{\\text{peak,NaP}}(m_{NaP}^{\\infty}(V))^3(V-E_{NaP})\n",
    "\\end{equation}\n",
    "This equation is also given in a comment in Synthesis, but is missing from the implementation.\n",
    "\n",
    "**Note: NEST implements the equation according to Compte et al (2003) with $N_{NaP}=3$, while Synthesis uses $N_{NaP}=1$.**\n",
    "\n",
    "\n",
    "#### Depolarization-activated Potassium Current $I_{DK}$\n",
    "\n",
    "Synthesis: `IKNaChannel`\n",
    "\n",
    "This model also only has a single activation variable $m$, following more complicated dynamics expressed by $D$.\n",
    "\n",
    "##### Equations in paper\n",
    "\n",
    "\\begin{align}\n",
    " dD/dt &= D_{\\text{influx}} - D(1-D_{\\text{eq}})/\\tau_D \\\\\n",
    " D_{\\text{influx}} &= 1/\\{1+ \\exp[-(V-D_{\\theta})/\\sigma_D]\\} \\\\\n",
    " m_{DK}^{\\infty} &= 1/1 + (d_{1/2}D)^{3.5}\n",
    "\\end{align}\n",
    "\n",
    "There are several problems with these equations.\n",
    "\n",
    "In the steady state the first equation becomes\n",
    "\\begin{equation}\n",
    " 0 = - D(1-D_{\\text{eq}})/\\tau_D \n",
    " \\end{equation}\n",
    " with solution\n",
    " \\begin{equation}\n",
    " D = 0\n",
    "\\end{equation}\n",
    "This contradicts both the statement [HT05, p. 1679] that $D\\to D_{\\text{eq}}$ in this case, and the requirement that $D>0$ to avoid a singluarity in the equation for $m_{DK}^{\\infty}$. The most plausible correction is\n",
    "\\begin{equation}\n",
    " dD/dt = D_{\\text{influx}} - (D-D_{\\text{eq}})/\\tau_D \n",
    "\\end{equation}\n",
    "\n",
    "The third equation appears incorrect and logic as well as Wang et al, *J Neurophysiol* 89:3279–3293, 2003, Eq 9, cited in [HT05, p 1679], indicate that the correct equation is\n",
    "\n",
    "\\begin{equation}\n",
    " m_{DK}^{\\infty} = 1/(1 + (d_{1/2} / D)^{3.5})\n",
    "\\end{equation}\n",
    "\n",
    "\n",
    "\n",
    "##### Corrected equations\n",
    "\n",
    "The equations for this channel implemented in NEST are thus\n",
    "\n",
    "\\begin{align}\n",
    "I_{DK} &= - g_{\\text{peak},DK} m_{DK}(V,t) (V - E_{DK})\\\\\n",
    " m_{DK} &= \\frac{1}{1 + \\left(\\frac{d_{1/2}}{D}\\right)^{3.5}}\\\\\n",
    " \\frac{dD}{dt} &= D_{\\text{influx}}(V) - \\frac{D-D_{\\text{eq}}}{\\tau_D} = \\frac{D_{\\infty}(V)-D}{\\tau_D} \\\\\n",
    " D_{\\infty}(V) &= \\tau_D D_{\\text{influx}}(V) + {D_{\\text{eq}}}\\\\\n",
    " D_{\\text{influx}} &= \\frac{D_{\\text{influx,peak}}}{1+ \\exp\\left(-\\frac{V-D_{\\theta}}{\\sigma_D}\\right)} \n",
    "\\end{align}\n",
    "\n",
    "with \n",
    "\n",
    "|$D_{\\text{influx,peak}}$|$D_{\\text{eq}}$|$\\tau_D$|$D_{\\theta}$|$\\sigma_D$|$d_{1/2}$|\n",
    "| --: | --: | --: | --: | --: | --: |\n",
    "|$0.025\\text{ms}^{-1}$ |$0.001$|$1250\\text{ms}$|$-10\\text{mV}$|$5\\text{mV}$|$0.25$|\n",
    "\n",
    "Note the following:\n",
    "- $D_{eq}$ is the equilibrium value only for $D_{\\text{influx}}(V)=0$, i.e., in the limit $V\\to -\\infty$ and $t\\to\\infty$.\n",
    "- The actual steady-state value is $D_{\\infty}$.\n",
    "- $d_{1/2}$, $D$, $D_{\\infty}$, and $D_{\\text{eq}}$ have identical, but arbitrary units, so we can assume them dimensionless ($D$ is a \"factor\" that in an abstract way represents concentrations).\n",
    "- $D_{\\text{influx}}$ and $D_{\\text{influx,peak}}$ are rates of change of $D_{\\infty}$ and thus have units of inverse time.\n",
    "- $m_{DK}$ is a steep sigmoid which is almost 0 or 1 except for a narrow window around $d_{1/2}$.\n",
    "- To the left of this window, $I_{DK}\\approx 0$.\n",
    "- To the right of this window, $I_{DK}\\sim -(V-E_{DK})$.\n",
    "- $m_{DK}$ is not integrated over time, instead it is an instantaneous transform of $D$, which is integrated over time.\n",
    "\n",
    "**Note: The differential equation for $dD/dt$ differs from the one implemented in Synthesis.**"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Synaptic channels\n",
    "\n",
    "These are described in [HT05, p 1678]. Synaptic channels are conductance based with double-exponential time course (beta functions) and normalized for peak conductance. NMDA channels are additionally voltage gated, as described below.\n",
    "\n",
    "Let $\\{t_{(j, X)}\\}$ be the set of all spike arrival times, where $X$ indicates the synapse model and $j$ enumerates spikes. Then the total synaptic input is given by\n",
    "\n",
    "\\begin{equation}\n",
    "I_{\\text{syn}}(t) = - \\sum_{\\{t_{(j, X)}\\}} \\bar{g}_X(t-t_{(j, X)}) (V-E_X)\n",
    "\\end{equation}\n",
    "\n",
    "#### Standard Channels\n",
    "\n",
    "Synthesis: `SynChannel`\n",
    "\n",
    "The conductance change due to a single input spike at time $t=0$ through a channel of type $X$ is given by (see below for exceptions)\n",
    "\n",
    "\\begin{align}\n",
    "    \\bar{g}_X(t) &= g_X(t)\\\\\n",
    "    g_X(t) &= g_{\\text{peak}, X}\\frac{\\exp(-t/\\tau_1) - \\exp(-t/\\tau_2)}{\n",
    "                 \\exp(-t_{\\text{peak}}/\\tau_1) - \\exp(-t_{\\text{peak}}/\\tau_2)} \\Theta(t)\\\\\n",
    "     t_{\\text{peak}} &= \\frac{\\tau_2 \\tau_1}{\\tau_2 - \\tau_1} \\ln\\frac{ \\tau_2}{\\tau_1}\n",
    "\\end{align} \n",
    "\n",
    "where $t_{\\text{peak}}$ is the time of the conductance maximum and $\\tau_1$ and $\\tau_2$ are synaptic rise- and decay-time, respectively; $\\Theta(t)$ is the Heaviside step function. The equation is integrated using exact integration in Synthesis; in NEST, it is included in the ODE-system integrated using the Runge-Kutta-Fehlberg 4(5) solver from GSL.\n",
    "\n",
    "The \"indirection\" from $g$ to $\\bar{g}$ is required for consistent notation for NMDA channels below.\n",
    "\n",
    "These channels are used for AMPA, GABA_A and GABA_B channels.\n",
    "\n",
    "#### NMDA Channels\n",
    "\n",
    "Synthesis: `SynNMDAChannel`\n",
    "\n",
    "For the NMDA channel we have\n",
    "\\begin{equation}\n",
    "\\bar{g}_{\\text{NMDA}}(t) = m(V, t) g_{\\text{NMDA}}(t)\n",
    "\\end{equation}\n",
    "with $g_{\\text{NMDA}}(t)$ from above. \n",
    "\n",
    "The voltage-dependent gating $m(V, t)$ is defined as follows (based on textual description, Vargas-Caballero and Robinson *J Neurophysiol* 89:2778–2783, 2003, [doi:10.1152/jn.01038.2002](http://dx.doi.org/10.1152/jn.01038.2002), and code inspection):\n",
    "\n",
    "\\begin{align}\n",
    "     m(V, t) &= a(V) m_{\\text{fast}}^*(V, t) + ( 1 - a(V) ) m_{\\text{slow}}^*(V, t)\\\\\n",
    "     a(V)    &= 0.51 - 0.0028 V \\\\\n",
    "     m^{\\infty}(V) &= \\frac{1}{ 1 + \\exp\\left( -S_{\\text{act}} ( V - V_{\\text{act}} ) \\right) } \\\\\n",
    "     m_X^*(V, t) &= \\min(m^{\\infty}(V), m_X(V, t))\\\\\n",
    "      \\frac{\\text{d}m_X}{\\text{d}t} &= \\frac{m^{\\infty}(V) - m_X }{ \\tau_{\\text{Mg}, X}}\n",
    "\\end{align} \n",
    "\n",
    "where $X$ is \"slow\" or \"fast\". $a(V)$ expresses voltage-dependent weighting between slow and fast unblocking, $m^{\\infty}(V)$ the steady-state value of the proportion of unblocked NMDA-channels, the minimum condition in $m_X^*(V,t)$ the instantaneous blocking and the differential equation for $m_X(V,t)$ the unblocking dynamics.\n",
    "\n",
    "Synthesis uses tabluated values for $m^{\\infty}$. NEST uses the best fit of $V_{\\text{act}}$ and $S_{\\text{act}}$  to the tabulated data for conductance table `fNMDA`.\n",
    "\n",
    "**Note**: NEST also supports instantaneous NMDA dynamics using a boolean switch. In that case $m(V, t)=m^{\\infty}(V)$. \n",
    "\n",
    "### No synaptic \"minis\"\n",
    "\n",
    "Synaptic \"minis\" due to spontaneous release of neurotransmitter quanta [HT05, p 1679] are not included in the NEST implementation of the Hill-Tononi model, because the total mini input rate for a cell was just 2 Hz and they cause PSP changes by $0.5 \\pm 0.25$mV only and thus should have minimal effect."
   ]
  },
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   "metadata": {},
   "source": [
    "## The Synapse Depression Model\n",
    "\n",
    "The synapse depression model is implemented in NEST as `ht_synapse`, in Synthesis in `SynChannel` and `VesiclePool`.\n",
    "\n",
    "$P\\in[0, 1]$ describes the state of the presynaptic vesicle pool. Spikes are transmitted with an effective weight\n",
    "\\begin{equation}\n",
    "w_{\\text{eff}} = P w\n",
    "\\end{equation}\n",
    "where $w$ is the nominal weight of the synapse.\n",
    "\n",
    "### Evolution of $P$ in paper and Synthesis implementation\n",
    "\n",
    "According to [HT05, p 1678], the pool $P$ evolves according to\n",
    "\\begin{equation}\n",
    "\\frac{\\text{d}P}{\\text{d}t} = -\\:\\text{spike}\\:\\delta_P P+\\frac{P_{\\text{peak}}-P}{\\tau_P}\n",
    "\\end{equation}\n",
    "where\n",
    "- $\\text{spike}=1$ while the neuron is in spiking state, 0 otherwise\n",
    "- $P_{\\text{peak}}=1$ \n",
    "- $\\delta_P = 0.5$ by default\n",
    "- $\\tau_P = 500\\text{ms}$ by default\n",
    "Since neurons are in spiking state for one integration time step $\\Delta t$, this suggest that the effect of a spike on the vesicle pool is approximately\n",
    "\\begin{equation}\n",
    "P \\leftarrow ( 1 - \\Delta t \\delta_P ) P\n",
    "\\end{equation}\n",
    "For default parameters $\\Delta t=0.25\\text{ms}$ and $\\delta_P=0.5$, this means that a single spike reduceds the pool by 1/8 of its current size.\n",
    "\n",
    "### Evolution of $P$ in the NEST implementation\n",
    "\n",
    "In NEST, we modify the equations above to obtain a definite jump in pool size on transmission of a spike, without any dependence on the integration time step (fixing explicitly $P_{\\text{peak}}$):\n",
    "\n",
    "\\begin{align}\n",
    "\\frac{\\text{d}P}{\\text{d}t} &= \\frac{1-P}{\\tau_P} \\\\\n",
    "P &\\leftarrow ( 1 - \\delta_P^*) P \n",
    "\\end{align}\n",
    "\n",
    "$P$ is only updated when a spike passes the synapse, in the following way (where $\\Delta$ is the time since the last spike through the same synapse):\n",
    "\n",
    "1. Recuperation: $P\\leftarrow 1 - ( 1 - P ) \\exp( -\\Delta / \\tau_P )$\n",
    "2. Spike transmission with $w_{\\text{eff}} = P w$\n",
    "3. Depletion: $P \\leftarrow ( 1 - \\delta_P^*) P$\n",
    "\n",
    "To achieve approximately the same depletion as in Synthesis, use $\\delta_P^*=\\Delta t\\delta_p$.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tests of the Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "import math\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy.optimize as so\n",
    "import scipy.integrate as si\n",
    "import matplotlib.pyplot as plt\n",
    "import nest\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (12, 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Neuron Model\n",
    "\n",
    "#### Passive properties\n",
    "\n",
    "Test relaxation of neuron and threshold to equilibrium values in absence of intrinsic currents and input. We then have\n",
    "\\begin{align}\n",
    "\\tau_m \\dot{V}&= \\left[-g_{NaL}(V-E_{Na})-g_{KL}(V-E_K)\\right] = -(g_{NaL}+g_{KL})V+(g_{NaL}E_{Na}+g_{KL}E_K)\\\\\n",
    "\\Leftrightarrow\\quad \\tau_{\\text{eff}}\\dot{V} &= -V+V_{\\infty}\\\\\n",
    "V_{\\infty} &= \\frac{g_{NaL}E_{Na}+g_{KL}E_K}{g_{NaL}+g_{KL}}\\\\\n",
    "\\tau_{\\text{eff}}&=\\frac{\\tau_m}{g_{NaL}+g_{KL}}\n",
    "\\end{align}\n",
    "with solution\n",
    "\\begin{equation}\n",
    "V(t) = V_0 e^{-\\frac{t}{\\tau_{\\text{eff}}}} + V_{\\infty}\\left(1-e^{-\\frac{t}{\\tau_{\\text{eff}}}} \\right)\n",
    "\\end{equation}\n",
    "and for the threshold\n",
    "\\begin{equation}\n",
    "\\theta(t) = \\theta_0 e^{-\\frac{t}{\\tau_{\\theta}}} + \\theta_{eq}\\left(1-e^{-\\frac{t}{\\tau_{\\theta}}} \\right)\n",
    "\\end{equation}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def Vpass(t, V0, gNaL, ENa, gKL, EK, taum, I=0):\n",
    "    tau_eff = taum/(gNaL + gKL)\n",
    "    Vinf = (gNaL*ENa + gKL*EK + I)/(gNaL + gKL)\n",
    "    return V0*np.exp(-t/tau_eff) + Vinf*(1-np.exp(-t/tau_eff))\n",
    "\n",
    "def theta(t, th0, theq, tauth):\n",
    "    return th0*np.exp(-t/tauth) + theq*(1-np.exp(-t/tauth))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vex  = -76.694, Vsim  = -76.694, Vex-Vsim   = -1.847e-13\n",
      "thex = -52.895, thsim = -52.895, thex-thsim = -3.553e-13\n",
      "Vex  = -70.000, Vsim  = -70.000, Vex-Vsim   = 0.000e+00\n",
      "thex = -51.000, thsim = -51.000, thex-thsim = 0.000e+00\n",
      "Vex  = -66.653, Vsim  = -66.653, Vex-Vsim   = 1.137e-13\n",
      "thex = -45.451, thsim = -45.451, thex-thsim = 1.009e-12\n"
     ]
    }
   ],
   "source": [
    "nest.ResetKernel()\n",
    "nest.SetDefaults('ht_neuron', {'g_peak_NaP': 0., 'g_peak_KNa': 0.,\n",
    "                               'g_peak_T': 0., 'g_peak_h': 0.,\n",
    "                               'tau_theta': 10.})\n",
    "hp = nest.GetDefaults('ht_neuron')\n",
    "\n",
    "V_th_0 = [(-100., -65.), (-70., -51.), (-55., -10.)]\n",
    "T_sim = 20.\n",
    "\n",
    "nrns = nest.Create('ht_neuron', n=len(V_th_0), params=[{'V_m': V, 'theta': th} \n",
    "                                               for V, th in V_th_0])\n",
    "nest.Simulate(T_sim)\n",
    "V_th_sim = nest.GetStatus(nrns, ['V_m', 'theta'])\n",
    "\n",
    "for (V0, th0), (Vsim, thsim) in zip(V_th_0, V_th_sim):\n",
    "    Vex = Vpass(T_sim, V0, hp['g_NaL'], hp['E_Na'], hp['g_KL'], hp['E_K'], hp['tau_m'])\n",
    "    thex = theta(T_sim, th0, hp['theta_eq'], hp['tau_theta'])\n",
    "    print('Vex  = {:.3f}, Vsim  = {:.3f}, Vex-Vsim   = {:.3e}'.format(Vex, Vsim, Vex-Vsim))\n",
    "    print('thex = {:.3f}, thsim = {:.3f}, thex-thsim = {:.3e}'.format(thex, thsim, thex-thsim))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Agreement is excellent."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Spiking without intrinsic currents or synaptic input\n",
    "\n",
    "The equations above hold for input current $I(t)$, but with\n",
    "\\begin{equation}\n",
    "V_{\\infty}(I) = \\frac{g_{NaL}E_{Na}+g_{KL}E_K}{g_{NaL}+g_{KL}} + \\frac{I}{g_{NaL}+g_{KL}}\n",
    "\\end{equation}\n",
    "In NEST, we need to inject input current into the `ht_neuron` with a `dc_generator`, whence the current will set on only at a later time and we need to take this into account. For simplicity, we assume that $V$ is initialized to $V_{\\infty}(I=0)$ and that current onset is at $t_I$. We then have for $t\\geq t_I$\n",
    "\\begin{equation}\n",
    "V(t) = V_{\\infty}(0) e^{-\\frac{t-t_I}{\\tau_{\\text{eff}}}} + V_{\\infty}(I)\\left(1-e^{-\\frac{t-t_I}{\\tau_{\\text{eff}}}} \\right)\n",
    "\\end{equation}\n",
    "If we also initialize $\\theta=\\theta_{\\text{eq}}$, the threshold is constant and we have the first spike at\n",
    "\\begin{align}\n",
    "V(t) &= \\theta_{\\text{eq}}\\\\\n",
    "\\Leftrightarrow\\quad t &= t_I -\\tau_{\\text{eff}} \\ln \\frac{\\theta_{\\text{eq}}-V_{\\infty}(I)}{V_{\\infty}(0)-V_{\\infty}(I)}\n",
    "\\end{align}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def t_first_spike(gNaL, ENa, gKL, EK, taum, theq, tI, I):\n",
    "    tau_eff = taum/(gNaL + gKL)\n",
    "    Vinf0 = (gNaL*ENa + gKL*EK)/(gNaL + gKL)\n",
    "    VinfI = (gNaL*ENa + gKL*EK + I)/(gNaL + gKL)\n",
    "    return tI - tau_eff * np.log((theq-VinfI) / (Vinf0-VinfI))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tex  = 34.4056, tsim  = 34.4060, tex-tsim = -0.0004\n",
      "tex  = 10.1174, tsim  = 10.1180, tex-tsim = -0.0006\n",
      "tex  = 5.4503, tsim  = 5.4510, tex-tsim = -0.0007\n"
     ]
    }
   ],
   "source": [
    "nest.ResetKernel()\n",
    "nest.SetKernelStatus({'resolution': 0.001})\n",
    "nest.SetDefaults('ht_neuron', {'g_peak_NaP': 0., 'g_peak_KNa': 0.,\n",
    "                               'g_peak_T': 0., 'g_peak_h': 0.})\n",
    "hp = nest.GetDefaults('ht_neuron')\n",
    "\n",
    "I = [25., 50., 100.]\n",
    "tI = 1.\n",
    "delay = 1.\n",
    "T_sim = 40.\n",
    "\n",
    "nrns = nest.Create('ht_neuron', n=len(I))\n",
    "dcgens = nest.Create('dc_generator', n=len(I), params=[{'amplitude': dc,\n",
    "                                                        'start': tI} for dc in I])\n",
    "sdets = nest.Create('spike_detector', n=len(I))\n",
    "nest.Connect(dcgens, nrns, 'one_to_one', {'delay': delay})\n",
    "nest.Connect(nrns, sdets, 'one_to_one')\n",
    "nest.Simulate(T_sim)\n",
    "\n",
    "t_first_sim = [ev['events']['times'][0] for ev in nest.GetStatus(sdets)]\n",
    "\n",
    "for dc, tf_sim in zip(I, t_first_sim):\n",
    "    tf_ex = t_first_spike(hp['g_NaL'], hp['E_Na'], hp['g_KL'], hp['E_K'], \n",
    "                          hp['tau_m'], hp['theta_eq'], tI+delay, dc)\n",
    "    print('tex  = {:.4f}, tsim  = {:.4f}, tex-tsim = {:.4f}'.format(tf_ex, \n",
    "                                                                    tf_sim, \n",
    "                                                                    tf_ex-tf_sim))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Agreement is as good as possible: All spikes occur in NEST at then end of the time step containing the expected spike time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Inter-spike interval\n",
    "\n",
    "After each spike, $V_m = \\theta = E_{Na}$, i.e., all memory is erased. We can thus treat ISIs independently. $\\theta$ relaxes according to the equation above. For $V_m$, we have during $t_{\\text{spike}}$ after a spike\n",
    "\n",
    "\\begin{align}\n",
    "\\tau_m\\dot{V} &= {-g_{\\text{NaL}}(V-E_{\\text{Na}})\n",
    "-g_{\\text{KL}}(V-E_{\\text{K}})+I}\n",
    "-\\frac{\\tau_m}{\\tau_{\\text{spike}}}({V-E_{\\text{K}}})\\\\\n",
    "&=  -(g_{NaL}+g_{KL}+\\frac{\\tau_m}{\\tau_{\\text{spike}}})V+(g_{NaL}E_{Na}+g_{KL}E_K+\\frac{\\tau_m}{\\tau_{\\text{spike}}}E_K)\n",
    "\\end{align}\n",
    "\n",
    "thus recovering the same for for the solution but with\n",
    "\n",
    "\\begin{align}\n",
    "\\tau^*_{\\text{eff}} &= \\frac{\\tau_m}{g_{NaL}+g_{KL}+\\frac{\\tau_m}{\\tau_{\\text{spike}}}}\\\\\n",
    "V^*_{\\infty} &= \\frac{g_{NaL}E_{Na}+g_{KL}E_K+I+\\frac{\\tau_m}{\\tau_{\\text{spike}}}E_K}{g_{NaL}+g_{KL}+\\frac{\\tau_m}{\\tau_{\\text{spike}}}}\n",
    "\\end{align}\n",
    "\n",
    "Assuming that the ISI is longer than the refractory period $t_{\\text{spike}}$, and we had a spike at time $t_s$, then we have at $t_s+t_{\\text{spike}}$\n",
    "\n",
    "\\begin{align}\n",
    "V^* &= V(t_s+t_{\\text{spike}}) = E_{Na} e^{-\\frac{t_{\\text{spike}}}{\\tau^*_{\\text{eff}}}} + V^*_{\\infty}(I)\\left(1-e^{-\\frac{t_{\\text{spike}}}{\\tau^*_{\\text{eff}}}} \\right)\\\\\n",
    "\\theta^* &= \\theta(t_s+t_{\\text{spike}}) = E_{Na} e^{-\\frac{t_{\\text{spike}}}{\\tau_{\\theta}}} + \\theta_{eq}\\left(1-e^{-\\frac{t_{\\text{spike}}}{\\tau_{\\theta}}} \\right)\\\\\n",
    "t^* &= t_s+t_{\\text{spike}}\n",
    "\\end{align}\n",
    "\n",
    "For $t>t^*$, the normal equations apply again, i.e.,\n",
    "\\begin{align}\n",
    "V(t) &= V^* e^{-\\frac{t-t^*}{\\tau_{\\text{eff}}}} + V_{\\infty}(I)\\left(1-e^{-\\frac{t-t^*}{\\tau_{\\text{eff}}}} \\right)\\\\\n",
    "\\theta(t) &= \\theta^* e^{-\\frac{t-t^*}{\\tau_{\\theta}}} + \\theta_{\\infty}\\left(1-e^{-\\frac{t-t^*}{\\tau_{\\theta}}}\\right)\n",
    "\\end{align}\n",
    "\n",
    "The time of the next spike is then given by\n",
    "\\begin{equation}\n",
    "V(\\hat{t}) = \\theta(\\hat{t})\n",
    "\\end{equation}\n",
    "which can only be solved numerically. The ISI is then obtained as $\\hat{t}-t_s$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def Vspike(tspk, gNaL, ENa, gKL, EK, taum, tauspk, I=0):\n",
    "    tau_eff = taum/(gNaL + gKL + taum/tauspk)\n",
    "    Vinf = (gNaL*ENa + gKL*EK + I + taum/tauspk*EK)/(gNaL + gKL + taum/tauspk)\n",
    "    return ENa*np.exp(-tspk/tau_eff) + Vinf*(1-np.exp(-tspk/tau_eff))\n",
    "\n",
    "def thetaspike(tspk, ENa, theq, tauth):\n",
    "    return ENa*np.exp(-tspk/tauth) + theq*(1-np.exp(-tspk/tauth))\n",
    "\n",
    "def Vpost(t, tspk, gNaL, ENa, gKL, EK, taum, tauspk, I=0):\n",
    "    Vsp = Vspike(tspk, gNaL, ENa, gKL, EK, taum, tauspk, I)\n",
    "    return Vpass(t-tspk, Vsp, gNaL, ENa, gKL, EK, taum, I)\n",
    "\n",
    "def thetapost(t, tspk, ENa, theq, tauth):\n",
    "    thsp = thetaspike(tspk, ENa, theq, tauth)\n",
    "    return theta(t-tspk, thsp, theq, tauth)\n",
    "\n",
    "def threshold(t, tspk, gNaL, ENa, gKL, EK, taum, tauspk, I, theq, tauth):\n",
    "    return Vpost(t, tspk, gNaL, ENa, gKL, EK, taum, tauspk, I) - thetapost(t, tspk, ENa, theq, tauth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "isi_ex  = 14.3144, isi_sim (min, mean, max)  = (14.3150, 14.3150, 14.3150)\n",
      "isi_ex  = 5.6602, isi_sim (min, mean, max)  = (5.6610, 5.6610, 5.6610)\n",
      "isi_ex  = 3.9718, isi_sim (min, mean, max)  = (3.9720, 3.9720, 3.9720)\n"
     ]
    }
   ],
   "source": [
    "nest.ResetKernel()\n",
    "nest.SetKernelStatus({'resolution': 0.001})\n",
    "nest.SetDefaults('ht_neuron', {'g_peak_NaP': 0., 'g_peak_KNa': 0.,\n",
    "                               'g_peak_T': 0., 'g_peak_h': 0.})\n",
    "hp = nest.GetDefaults('ht_neuron')\n",
    "\n",
    "I = [25., 50., 100.]\n",
    "tI = 1.\n",
    "delay = 1.\n",
    "T_sim = 1000.\n",
    "\n",
    "nrns = nest.Create('ht_neuron', n=len(I))\n",
    "dcgens = nest.Create('dc_generator', n=len(I), params=[{'amplitude': dc,\n",
    "                                                        'start': tI} for dc in I])\n",
    "sdets = nest.Create('spike_detector', n=len(I))\n",
    "nest.Connect(dcgens, nrns, 'one_to_one', {'delay': delay})\n",
    "nest.Connect(nrns, sdets, 'one_to_one')\n",
    "nest.Simulate(T_sim)\n",
    "\n",
    "isi_sim = []\n",
    "for ev in nest.GetStatus(sdets):\n",
    "    t_spk = ev['events']['times']\n",
    "    isi = np.diff(t_spk)\n",
    "    isi_sim.append((np.min(isi), np.mean(isi), np.max(isi)))\n",
    "\n",
    "for dc, (isi_min, isi_mean, isi_max) in zip(I, isi_sim):\n",
    "    isi_ex = so.bisect(threshold, hp['t_ref'], 50, \n",
    "                      args=(hp['t_ref'], hp['g_NaL'], hp['E_Na'], hp['g_KL'], hp['E_K'], \n",
    "                          hp['tau_m'], hp['tau_spike'], dc, hp['theta_eq'], hp['tau_theta']))\n",
    "    print('isi_ex  = {:.4f}, isi_sim (min, mean, max)  = ({:.4f}, {:.4f}, {:.4f})'.format(\n",
    "        isi_ex, isi_min, isi_mean, isi_max))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- ISIs are as predicted: measured ISI is predicted rounded up to next time step\n",
    "- ISIs are perfectly regular as expected"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Intrinsic Currents\n",
    "\n",
    "##### Preparations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nest.ResetKernel()\n",
    "class Channel:\n",
    "    \"\"\"\n",
    "    Base class for channel models in Python.\n",
    "    \"\"\"\n",
    "    def tau_m(self, V):\n",
    "        raise NotImplementedError()\n",
    "    def tau_h(self, V):\n",
    "        raise NotImplementedError()\n",
    "    def m_inf(self, V):\n",
    "        raise NotImplementedError()\n",
    "    def h_inf(self, V):\n",
    "        raise NotImplementedError()\n",
    "    def D_inf(self, V):\n",
    "        raise NotImplementedError()\n",
    "    def dh(self, h, t, V):\n",
    "        return (self.h_inf(V)-h)/self.tau_h(V)\n",
    "    def dm(self, m, t, V):\n",
    "        return (self.m_inf(V)-m)/self.tau_m(V)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def voltage_clamp(channel, DT_V_seq, nest_dt=0.1):\n",
    "    \"Run voltage clamp with voltage V through intervals DT.\"\n",
    "\n",
    "    # NEST part\n",
    "    nest_g_0 = {'g_peak_h': 0., 'g_peak_T': 0., 'g_peak_NaP': 0., 'g_peak_KNa': 0.}\n",
    "    nest_g_0[channel.nest_g] = 1.\n",
    "    \n",
    "    nest.ResetKernel()\n",
    "    nest.SetKernelStatus({'resolution': nest_dt})\n",
    "    nrn = nest.Create('ht_neuron', params=nest_g_0)\n",
    "    mm = nest.Create('multimeter', params={'record_from': ['V_m', 'theta', channel.nest_I],\n",
    "                                           'interval': nest_dt})\n",
    "    nest.Connect(mm, nrn)\n",
    "\n",
    "    # ensure we start from equilibrated state\n",
    "    nest.SetStatus(nrn, {'V_m': DT_V_seq[0][1], 'equilibrate': True,\n",
    "                         'voltage_clamp': True})\n",
    "    for DT, V in DT_V_seq:\n",
    "        nest.SetStatus(nrn, {'V_m': V, 'voltage_clamp': True})\n",
    "        nest.Simulate(DT)\n",
    "    t_end = nest.GetKernelStatus()['time']\n",
    "    \n",
    "    # simulate a little more so we get all data up to t_end to multimeter\n",
    "    nest.Simulate(2 * nest.GetKernelStatus()['min_delay'])\n",
    "    \n",
    "    tmp = pd.DataFrame(nest.GetStatus(mm)[0]['events'])\n",
    "    nest_res = tmp[tmp.times <= t_end]\n",
    "    \n",
    "    # Control part\n",
    "    t_old = 0.\n",
    "    try:\n",
    "        m_old = channel.m_inf(DT_V_seq[0][1])\n",
    "    except NotImplementedError:\n",
    "        m_old = None\n",
    "    try:\n",
    "        h_old = channel.h_inf(DT_V_seq[0][1])\n",
    "    except NotImplementedError:\n",
    "        h_old = None\n",
    "    try:\n",
    "        D_old = channel.D_inf(DT_V_seq[0][1])\n",
    "    except NotImplementedError:\n",
    "        D_old = None\n",
    "        \n",
    "    t_all, I_all = [], []\n",
    "    if D_old is not None:\n",
    "        D_all = []\n",
    "        \n",
    "    for DT, V in DT_V_seq:\n",
    "        t_loc = np.arange(0., DT+0.1*nest_dt, nest_dt)\n",
    "        I_loc = channel.compute_I(t_loc, V, m_old, h_old, D_old)\n",
    "        t_all.extend(t_old + t_loc[1:])\n",
    "        I_all.extend(I_loc[1:])\n",
    "        if D_old is not None:\n",
    "            D_all.extend(channel.D[1:])\n",
    "        m_old = channel.m[-1] if m_old is not None else None\n",
    "        h_old = channel.h[-1] if h_old is not None else None\n",
    "        D_old = channel.D[-1] if D_old is not None else None\n",
    "        t_old = t_all[-1]\n",
    "        \n",
    "    if D_old is None:\n",
    "        ctrl_res = pd.DataFrame({'times': t_all, channel.nest_I: I_all})\n",
    "    else:\n",
    "        ctrl_res = pd.DataFrame({'times': t_all, channel.nest_I: I_all, 'D': D_all})\n",
    "\n",
    "    return nest_res, ctrl_res"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### I_h channel\n",
    "\n",
    "The $I_h$ current is governed by\n",
    "\\begin{align}\n",
    "I_h &= g_{\\text{peak}, h} m_h(V, t) (V-E_h) \\\\\n",
    "\\frac{\\text{d}m_h}{\\text{d}t} &= \\frac{m_h^{\\infty}-m_h}{\\tau_{m,h}(V)}\\\\\n",
    "m_h^{\\infty}(V) &= \\frac{1}{1+\\exp\\left(\\frac{V+75\\text{mV}}{5.5\\text{mV}}\\right)} \\\\\n",
    "\\tau_{m,h}(V) &= \\frac{1}{\\exp(-14.59-0.086V) + \\exp(-1.87  + 0.0701V)}\n",
    "\\end{align}\n",
    "\n",
    "We first inspect $m_h^{\\infty}(V)$ and $\\tau_{m,h}(V)$ to prepare for testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nest.ResetKernel()\n",
    "class Ih(Channel):\n",
    "    \n",
    "    nest_g = 'g_peak_h'\n",
    "    nest_I = 'I_h'\n",
    "    \n",
    "    def __init__(self, ht_params):\n",
    "        self.hp = ht_params\n",
    "        \n",
    "    def tau_m(self, V):\n",
    "        return 1/(np.exp(-14.59-0.086*V) + np.exp(-1.87  + 0.0701*V))\n",
    "    \n",
    "    def m_inf(self, V):\n",
    "        return 1/(1+np.exp((V+75)/5.5))\n",
    "\n",
    "    def compute_I(self, t, V, m0, h0, D0):\n",
    "        self.m = si.odeint(self.dm, m0, t, args=(V,))\n",
    "        return - self.hp['g_peak_h'] * self.m * (V - self.hp['E_rev_h'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Gxx7z2+TJQacREREREclcVJAQkeMqeFpBnmzzJC/f+DKrf1jD1cvOp2qbxYwdG3Sy2NG1\nKwweDHfeCa+8EnQaEREREZHMI2dqBpnxUgTH7uUc2yJ4n4jEmFaV2nDeO5+z5ozufHHB1dyx7DYm\nNZ9ErrhcQUeLCaNHw//+B+3bw3vvwYUXBp1IRERERCT2pXaGxDVAAvBHKrdWQIFohxWRYIwaBe8s\nLcGSji8zrdU0nlj3BC3ntmTnAS0xAZAjBzz7LJxzDrRuDT//HHQiEREREZHYl6oZEmF9UzvjwYzr\nIswjIjFmxQq47z6/NW1qNKUX55xxDtfOv5ZLnrqEJe2XUPH0ikHHDFz+/LB4MdStC23awMqVfp+I\niIiIiKQstTMkGgO/p+G4LYCf0h5HRGLJzz9Dhw7QpAkMG/bX/kZnN+KDbh9wJPEIdafX5d3v3w0u\nZAwpXdr3kfjqK+jcGRITg04kIiIiIhK7UlWQcI6VznE4tQd1jvec42DksUQkaIcPw403Qu7cMGcO\nxMUd+3rlYpV5v9v7VC9RnStnXcmsT2cFEzTGXHghzJsHCxfC8OFBpxERERERiV1pXmXDjFpmVE/y\n/GozFpnxgBm5oxtPRIIyfDisXg3PPQfFi6c85vS8p7Os0zJuqnETXRZ1Ydgbw0h0mhbQujU8+KBv\ndrlkSdBpRERERERiUyTLfj4OVAEwowLwHLAPuB7QYoAiWcCSJf6CeswYaNjwxGNzx+VmepvpjG0y\nltHvjabdgnbsO7QvY4LGsIED4eqr/a0bmzYFnUZEREREJPZEUpCoAnwSfnw98I5zdABuBtpGKZeI\nBGTTJn8R3aYNDBiQuveYGYMaDOKldi+x9H9LaTyzcbZfgcMMnnkGCheGG26Ag7qJTURERETkGJEU\nJCzJ+5oAS8OPfwTOiEYoEQnGoUPQrp2/iJ4xw19Up8U151zDOze/w/9++x/NZjfjjwN/pEvOzKJo\nUViwAD791M+YEBERERGRv0RSkPgIGG7GTcDlwKvh/eWBrdEKJiIZb8wY+PhjmD/fX0xHonbp2qzo\nvIJvfvuG5nOas+vgruiGzGRq14ZJk+CRR/yfq4iIiIiIeJEUJPoBtYBHgFHOsSG8/zpgdbSCiUjG\nWr8eRoyAu+6COnVO7Vi1StVi+U3L+fLXL2kxpwW7D+6OTshMqlcvv2JJt27w9ddBpxERERERiQ1p\nLkg4x2fOUd05CjtHKMlLg4Au0YsmIhnl4EHfN+K88+Cee6JzzItKX8R/bvoP/7ft/2g5tyV7EvZE\n58CZkBk88QSUKQPXXw/71PNTRERERCSiGRJ/MqOAGYXMKATkBvJGJ5aIZKT77vPf3M+aBbmjuHhv\nnTJ1WNZpGZ/+8imt5rZib8Le6B08kylY0PeT2LABevcOOo2IiIiISPDSXJAwo7wZr5qxF/gD2BHe\ndoZ/ikgmsmYNjB0LoRDUqBH949crW4/XO73Oui3raD2vdbZeEvT882HaNL/6xjPPBJ1GRERERCRY\nkcyQmA0UBf4NXAlcEd4ah39GnZnlMLP7zWyjme0zsw1mNjyFcSPM7OfwmOVmVinZ66eZ2VQz225m\nu81sgZmdmR6ZRTKDffugSxe4+GIYNCj9zlO/XH1e6/gaa39aS5t5bdh/aH/6nSzGdekCXbvCbbfB\nZ58FnUZEREREMiszu93MvjOz/Wb2vpldfJLxHc3sEzPbG75ufsrMTj/he0L2koWsUPhxZwvZadH8\nDJEUJC4AbnGO+c7xtnOsTLpFM1wSQ4GewG3AOcBgYLCZ/Tnx2cyGAL2BHkAdYC+wzMySTkCfBLQC\n2gKXAaWBF9Mps0jMu+su+PFHmDkTcuZM33M1PKshSzsuZc3mNVz93NUcPHwwfU8Ywx5+GKpU8f0k\n9mbfu1hEREREJEJm1g4YD8QDNYFP8de/ZxxnfANgJvAkcC5+UYo6wBMnOdU/gfzhx88AhU85fBKR\nXIJ8CJQDMrJX/CXAy86518PPfzCzDvg/wKPuAO53zi0BMLPO+GVIrwGeN7NC+FkdNzrnVobH3AJ8\naWZ1nHNrM+iziMSEt96CKVP8kpRVq2bMOS/7x2Usab+EFnNa0HNJT565+hnMLGNOHkPy5oXnn4da\ntaBfP3jyyaATiYiIiEgm0x943Dk3C8DMeuG/fP83MDaF8fWA75xzU8PPvzezx/Ff9p/IV8BoC9lb\ngAE3WMh2pTTQxfssaRFJQaIb8JgZZYD/Aw4dE8KRHpOQVwPdzayyc+5/ZnYB0AD/S8DMygMlgTf+\nyuF2mdkH+GLG88BF+M+bdMzXZvZDeIwKEpJt7NoFt9wCl18Offpk7Lkbl2/MU22eotPCTpxzxjkM\nbTg0YwPEiKpVfTGoRw9o0QKuvTboRCIiIiKSGZhZLqA28MDRfc45Z2Yr8Ne2KVkDjDKzFs6518ys\nBHA98OpJTtcLmIAvdjhgZPhncg7IkIJEcaAifrpG0pNb+GdcBMc8mTFAIeArMzuCv9VkmHPuufDr\nJcPn3prsfVvDrwGUABKcc8mrOUnHiGQLAwbAb7/5WRI5Tmmtnch0rNGRr7Z/xV1v3EXVYlX5V7V/\nZXyIGNCtG7z2GnTvDnXr+mVBRURERERO4gz8dXdK178pzn12zq02s07AfDPLg68FLMa3PTguF+9W\n42dXYCFLBKq4eLft1OL/JZJLkaeB9fjKSwWgfLKf6aEd0AG4EX9/TBdgkJndlE7nE8myli6F6dNh\nwgQoXz64HKHGIa4/93o6LezEui3rggsSIDN/u0aePNC5MyQmBp1IRERERLIiMzsXmAzcB9QCmuGv\n4R9Pw2HKA79GNZdzKc22OMEb/HKfFzjHhmgGOfE57QdgtHNuWpJ9w4COzrlzw7dsfAtc6Jz7LMmY\nt4H1zrn+ZtYYWAEUTTpLwsw2AROdc5NTOG8HYE7NmjUpW7bsMa+1b9+e9u3bR/NjiqS7HTvgvPPg\nggt8YSLo9g37Du3j8hmX8/Pun/mw+4eULlg62EABeeMNaNoUxoyBwSe7i09EREREsox58+Yxb968\nY/Zt3ryZ9evXg7/enZv8PeFbNvYBbZ1zi5PsnwEUds79bfqxmc0C8jjnbkiyrwHwLlDKOZd8tkWK\nLGRF8L0czyTZBIeM6iHxJn6ljQwrSAD5gCPJ9iUS/gNwzn1nZr/glyH9DCDcxLIucLRpx8fA4fCY\nheExVYGz8PfTHNeAAQPo2LFjVD6ISJAGD/arOkyfHnwxAiBfrnwsvnExFz95MW3mteGdW94hX658\nQcfKcFdeCQMHwvDh/nHt2kEnEhEREZGMkNIX3XPmzKFTp07HfY9z7pCZfYy/tl0MYL5T/JXAlOO8\nLR+QkGxfIn+1XzgpC1lrYA5QANjFsb0kMqyHxCvARDOqA5/z96aWi1N816l5BRhuZpuB/+KnmPQH\npicZMyk8ZgOwCbgf2Ay87HO5XWb2FDDBzHYAu/G/rFVaYUOyg7ff9oWIadNiq1dBqYKleKX9KzR8\npiFdFnVh/nXzyWEBNLYI2MiRfqZEhw6wbh3kz3/y94iIiIhItjUBmBEuTKzFXx/nA2YAmNlooLRz\nrkt4/CvAE+HVOJYBpYGJwAfOuV9Sec7x+BYOd7t4ty8aHyKSgsRj4Z/3pvBaejW17I0vMEzFTw35\nGZgW3udP7NxYM8uHvwemCH7qSQvnXNIqUH/8TIsFwGnA68Dt6ZBXJKbs3+9Xc2jY0P+MNTVL1WTO\ntXO4dv613PvWvYy8YmTQkTJc7twwdy7UrAn9+8MTJ1sRWkRERESyLefc82Z2BjACv4DDJ0Az59zR\nHg8lgXJJxs80swL4699xwE78CpRpWfKuDDAlWsUIiKAg4VxEjTBPiXNuL3BneDvRuPvwTTqO9/pB\noE94E8k2Ro6E77+Hl18OZlWN1LjmnGsYfeVohr4xlHPOOIdONY4/TS2rOroUaM+efinQf2XPxUdE\nREREJBWcc48Cjx7ntVtS2DeVv1oaRGIZcBGw8RSOcYxIZkiISCby2Wcwdizccw9UqxZ0mhMb3GAw\nX/32FV0Xd6Xy6ZWpW7Zu0JEyXPfu8PrrfknQOnVi6/YaEREREcnWXgUespCdS0rtG+Jdmts3pOq7\nUjP6mpEntQc1o5cZBdMaRkSi68gRf4FbpQoMTctkrICYGY//83FqlarFjS/eyM4DO4OOlOGSLgXa\npYuWAhURERGRmPEk/jaQe4EXgEVJtoWRHDC1MyQmAvOAA6kcPxb4D75xpIgEZOpU+PBDeO8936Mg\nM8gdl5u5186l5uM16bmkJ8+1fQ6LhSVBMlCxYjBzpl8KdPJk31NCRERERCRILt5F/ebv1BYkDHjD\njMOpHJ83wjwiEiXffw933w233gr16wedJm3KFy3Pk62f5IYFN9C0QlO61eoWdKQM16QJ9OsHd90F\nV10F550XdCIRERERkehKbUEilMbjvgz8nsb3iEiUOAe33QZFisDo0UGnicz1511P943d6ftaX+qX\nq8+5xc8NOlKGe+ABWLYMOnWCDz7IPLNcRERERCRrsJD1BZ5w8e5A+PFxuXg3Ja3HT1VBwrk0FyRE\nJEDz58PSpX5VjUKFgk4TuUnNJ7Hqx1W0W9COtd3WkjdX9pp8lTcvzJ4NdetCKASjRgWdSERERESy\nmf7AHHz7hhPdSOyA9ClIiEjm8dtv0LcvXHcdtGkTdJpTky9XPp5r+xx1ptdhwH8G8GirFFc1ytJq\n1YL77oN774VWrTLf7TciIiIiknm5eFc+pcfREvWmFCISrIEDISEBpqS5PhmbqpeozsRmE5n20TRe\n+vKloOMEYsgQP0vipptgz56g04iIiIiIRIcKEiJZyJtvwowZMG4clCoVdJro6Vm7J22rtaXr4q78\n8McPQcfJcDlzwqxZsHUrDBgQdBoRERERya4sZGdE83gqSIhkEQcOQK9ecOml8O9/B50musyMJ1s/\nSaHTCtHhxQ4cTkztgj9ZR6VKMGECPPEELFkSdBoRERERyW4sZGcDq6J5TBUkRLKIBx+ETZvgsccg\nRxb8m100b1HmXjuX9ze/z4iVI4KOE4ju3X0fiW7d4Ndfg04jIiIiItmFhex84D1gZjSPm+bLFjPM\njOvNeNSMBWa8lHSLZjgRSZ1vvvFLRA4aBOdm4dUxG5zVgFCjECPfGcnbm94OOk6GM4Pp0+HwYejZ\n0y/vKiIiIiKSnixk9YF3gFku3j0QzWNH8j3qJOBZoDywB/gj2SYiGcg5uPVWKFsWhg8POk36G9pw\nKJeffTm3vHwLexKyX4fHkiX9bRsLF/q+EiIiIiIi6ew/wLMu3t0d7QNHUpC4CbjWOVo4x83OcUvS\nLdoBReTE5szxzSwffRTy5g06TfqLyxHHU22eYtvebQx7Y1jQcQJx7bXQuTP06QPffx90GhERERHJ\n4vYCpSxkFu0DR1KQ+APYGO0gIpJ2v/8Od94J7dpBs2ZBp8k4FYpWYNQVo3h47cOs+iGqfXUyjSlT\noGhRuPlmSEwMOo2IiIiIZGENgIuAp6N94EgKEvcB8WZkg+9iRWLb0KFw8CBMnBh0kozXp04f6pWt\nR9fFXTlw+EDQcTJc4cIwcyasXAmTJgWdRkRERESyKhfvNgANgdoWsqnRPHYkBYnngaLANjM+N2Nd\n0i2a4UTk+Favhief9M0sS5UKOk3GO3rrxnc7v8u2q240agT9+8Pdd8N//xt0GhERERHJqly8+xm4\nHLgwmseNpCAxE6gNzAZeBF5OtolIOjt0yK+ycPHF0KtX0GmCU614Ne697F7GrhrLui3Zsx46ahRU\nrAidOkFCQtBpRERERCSrcvFuB9AkmsfMGcF7WgHNnOO9aAYRkdSbOBG++AI++gji4oJOE6zBDQaz\n4MsFdF3clbXd1pIrLlfQkTJUnjwwezbUqQOhkC9QiIiIiIikBxfv9kfzeJEUJH4EdkUzhIik3qZN\ncN99cMcdULNm0GmClysuF0+3eZqLn7yYsavGMuyy7LfyRs2a/r+Je++FVq2gfv2gE4mIiIhIVmUh\nyw2cSbI7Lly8+yGtx4rklo0BwFgzzo7gvSJyCpyD3r2hWDEYkT3bJqSoZqmaDG4wmBHvjOCLX78I\nOk4ghgzxsyQ6d4Y9e4JOIyIiIiJZjYWssoXsXWA/8D3wXXjbFP6ZZpEUJGYDjYFvzdhtxu9Jt0hC\niEjqLFyyRUyxAAAgAElEQVQIr74KDz8MBQoEnSa23Hv5vVQoWoGui7tyJPFI0HEyXM6c8OyzsGUL\nDBwYdBoRERERyYJmAInAP/F9JWuFt5rhn2kWyS0b/SI5kYicml27oE8faN0arr466DSxJ0/OPDzV\n5ikaPt2Qh9c+TL962e+fqkqVYPx4uPVWaNMGWrYMOpGIiIiIZCEXArVdvPsqWgdMc0HCOWamZpwZ\nQ4HHnGNnmlOJyN/ccw/s3OlnR5gFnSY21S9Xnz51+jDszWG0qdqGCkUrBB0pw/XsCYsXQ9eu8Pnn\ncMYZQScSERERkSziCyCq/3cZyQyJ1LobeB5UkBA5VR9/DI88Ag8+CP/4R9BpYtuoK0ex+JvF9Hil\nB8tvWo5ls+qNGTz1FJx/vp8p8fzzKmCJiIiISGQsZIWSPB0CjLWQ3Q18DhxKOtbFuzQvfhFJD4nU\n0v8Ci0TBkSP+W+/zz/cra8iJFchdgGmtpvHGd2/wwhcvBB0nEKVKwWOPwYIFMGdO0GlEREREJBPb\nCewIb8uBesAbwLYk+4+OSbP0nCEhIlEwdSqsWwerV0OuXEGnyRyaV2rONedcw53L7qRl5ZYUyJ39\nOoBefz107Ai33w6XXqqZNSIiIiISkcbpeXAVJERi2E8/wfDhfoZEvXpBp8lcJjabSLWp1Rj5zkjG\nNBkTdJxAPPIIvPuuXwr0zTchLi7oRCIiIiKSmbh4tzKt77GQPQrc6+Ld9pONTc9bNkTkFN1xB+TL\nB6NHB50k8zm7yNnc3fBuJqyZwNfbvw46TiCKFIFZs3xRYty4oNOIiIiISDbRCSh00lGoICESs159\nFV58ESZO9BeWknaDGgzirMJn0ee1Pjjngo4TiMsvh8GD/Sot69YFnUZEREREsoFU95NMz4LEu8D+\ndDy+SJa1d6+/979pU7jxxqDTZF55cuZhcvPJLN+4nJe+fCnoOIEZMcI3Re3YEfbtCzqNiIiIiIiX\n5oKEGWedaDs6zjlaOseW6MYVyR5GjIBffoFHH9WSjaeqVZVWtK7Smv7L+rM3YW/QcQKRO7dfbWPT\nJhgyJOg0IiIiIiJeJDMkNgHfnWATkVPw+ecwYYJvZlmpUtBpsoZJzSexbe82Rr07KugogalWzfeR\neOQReO21oNOIiIiIiERWkKgJ1Eqy1QV6Ad8A10cvmkj2k5joV9SoVAkGDQo6TdZRoWgFhjYcyrjV\n4/jmt2+CjhOY226D5s3h3/+GX38NOo2IiIiIZHdpLkg4x6fJto+c40lgINA3+hFFso/p02HNGnjs\nMTjttKDTZC1DGgyhbKGy9H2tb7ZtcGkGzzwDhw9Djx6QTf8YRERERCR9zQZ2pWZgNJtafg1cHMXj\nHcPMSpvZs2a23cz2mdmnZlYr2ZgRZvZz+PXlZlYp2eunmdnU8DF2m9kCMzszvTKLpMWWLX41hJtv\n9isjSHTlzZWXSc0nsezbZSz6alHQcQJTsiQ8+SQsWgRPPRV0GhERERGJlJndbmbfmdl+M3vfzE54\nPW5muc1slJltMrMDZrbRzG5O0zlDlsdCVsdC9k8LWZuk29ExLt7d6uLd9tQcL2daTg5g9rf1RA0o\nBdwH/C+tx0vdOa0IsAp4A2gGbAcqAzuSjBkC9AY64/tcjASWmVk151xCeNgkoAXQFl+xmQq8CFya\nHrlF0uKOO3zzwXHjgk6SdbWu0pqWlVvSb1k/mlVqRr5c+YKOFIhrroFu3fx/c5dfDpUrB51IRERE\nRNLCzNoB44EewFqgP/76t4pzxy0GvAAUB24BvsVfx6d6koKFrDkwCzgjhZcdEJfqDxCW5oIEsDN8\nsqQM+BFIrwUKhwI/OOe6Jdn3fbIxdwD3O+eWAJhZZ2ArcA3wvJkVAv4N3OicWxkecwvwpZnVcc6t\nTafsIif1yivwwgt+JYRixYJOk3WZGZObT+a8R89j9Lujuf+K+4OOFJiJE+Gtt6BTJ3jvPciVK+hE\nIiIiIpIG/YHHnXOzAMysF9AKf807NvlgM2uO/yK+gnNuZ3j3D2k858P4osYIF++2Rho8qUhu2WgM\nXJFkawScC1R0jjXRCJWC1sBHZva8mW01s3Vm9mdxwszKAyXxMygAcM7tAj4ALgnvughfgEk65mv8\nL+HoGJEMt2vXX80G27cPOk3WV+n0SgyuP5ixq8fy3Y7suzBQgQIwdy6sWwf33ht0GhERERFJLTPL\nBdTm2GtbB6zg+Ne2rYGPgCFmttnMvjazh8wsTxpOXQKYEK1iBETW1HJlsu1d5/jKOQ5HK1QKKgC3\n4vtUXAVMA6aY2U3h10viZ20k/4PZGn4N/B9eQrhQcbwxIhlu2DD4/XeYNs03HZT0N7ThUM7IdwZD\nVgwJOkqg6tSBUaPgwQdhxYqg04iIiIhIKp2Bvz3iRNe/yVXAz5A4D38XwR3Adfg2Bqm1AD8hIWoi\nuWUDADPOBc4Ccifd7xyLTzVUCnIAa51z94Sff2pm5+OXG302Hc4nkiHWrIGpU2H8eDj77KDTZB/5\nc+dn9JWj6bKoC6t+WEWDsxoEHSkwAwfC8uVw003w6adwptr8ioiIiGRFOYBEoINzbg+Amd0JvGBm\ntznnDqbiGL2BFyxklwKfA4eSvuji3ZS0hoqkqWUFYCFQHT8r4eh3ukf7SqS5kUUqbAG+TLbvS+Da\n8ONfwjlKcGyVqASwPsmY3GZWKNksiRLh145r/PjxzJ8//5h97du3p73m18spSEiA7t2hdm3oqwVz\nM1ynGp2Y8sEU+i/rz/vd3ieHRXPRocwjRw6YNQsuuMCv8LJkid8nIiIiIulv3rx5zJs375h9mzdv\nPtnbtgNH8NeySZ3o2nYL8NPRYkTYl/jr6LL4Jpcn0x5/x8IB/EyJpL0lHZD+BQlgMvAdcGX4Zx2g\nGL7D58AIjpcaq4CqyfZVJdzY0jn3nZn9Es70GUC4iWVd/pqC8jFwODxmYXhMVfwsjxP2vhgwYAAd\nO3aMygcROWrsWPjqK/j4Y4hLjzKenFAOy8HEZhO5bMZlzP18Lp1qdAo6UmBKlYKZM6FlS5gyBfr1\nCzqRiIiISPaQ0hfdc+bMoVOn4/+/qXPukJl9jL+2XQxgZhZ+fryiwCrgOjPL55zbF95XFT9r4qQV\nkLBRQDwwxsW7xFS+54Qi+R7sEuBe59iOD5/oHO8BdxFBRSSVJgL1zOwuM6toZh2AbsAjScZMAoab\nWWszq45fjmQz8DL82eTyKWCCmTUys9rA08AqrbAhGe3rr+H++/10+QsuCDpN9nXpPy6lbbW23PXG\nXew7tO/kb8jCWrSAO++EwYN9o0sRERERiWkTgO5m1tnMzgEeA/IBMwDMbLSZzUwyfi7wG/CMmVUz\ns8vwq3E8lcrbNcC3a5gfrWIERFaQiAN2hx9vB0qHH3/P32cxRIVz7iPgX/gpIp8Dw4A7nHPPJRkz\nFr8MyeP41TXyAi2ccwlJDtUfWIJvxvE28DPQNj0yixxPYiL07AnlykF8fNBp5MEmD7Jt7zbGrx4f\ndJTAPfAAVK8ON94Ie/acfLyIiIiIBMM59zz+DoUR+DYFNYBmzrlfw0NKAuWSjN8LNAWKAB/iezG+\njG9umVozgXanHD6JSG7Z+D/gAvztGh8Ag81IAHoAG6OY7RjOuaXA0pOMuQ+47wSvHwT6hDeRQDz9\nNKxc6Vc1yJs36DRS8fSK9K3TlzGrxtC1VldKFyx98jdlUaedBvPmQa1a0KcPPPNM0IlERERE5Hic\nc48Cjx7ntVtS2PcN0OwUThkHDLaQNcO3Skje1PLOtB4wkhkSI5O8716gPPAu0JK0VVdEsp1ffoFB\ng3zzwCuvDDqNHDXssmHky5WP4W8ODzpK4KpU8Su/zJgBc+cGnUZEREREYkh1/GyMROB8oGaS7cJI\nDpjmGRLOsSzJ4w3AOWacDuxw7pgumyKSTN++kCsXjBsXdBJJqkieIoQahei9tDe96/SmVqlaQUcK\nVOfOfinQXr2gbl2oWDHoRCIiIiISNBfvGkf7mGmeIWHG02YUTLrPOX4H8pnxdNSSiWQxL70EL7wA\nkyZBsWJBp5HketTuQbXi1bhz2Z04l71rq2bw6KNQvDi0b++XqBURERERibZIbtnogm8YmVxeoPOp\nxRHJmn77DW69Fa6+2l/gSezJmSMn468az8rvV/Ly1y8HHSdwhQrBc8/BJ5/424xERERERKIt1QUJ\nMwqZURgwoGD4+dGtKL6HxLb0CiqSmfXtC4cOwbRp/ttniU3NKzWneaXmDFo+iIQjmhZw8cUwYQJM\nmeJn94iIiIiIRFNaZkjsBH4HHPANsCPJth14Gpga7YAimd2iRb454JQpUKpU0GnkZMY1Hcd3O77j\nkbWPBB0lJtx+O9xwA3TtCt98E3QaEREREclK0lKQaAxciZ8hcR1wRZKtIXCWc4yKekKRTOz3331j\nwNatoWPHoNNIapx35nn0qN2DEStHsH3f9qDjBM4Mpk+H0qXhuutg376gE4mIiIhIVpHqgoRzrHSO\nt/HLfC4KPz+6rXGOn9MtpUgm1bcvHDwIjz+uWzUyk1CjEA7HyHdGBh0lJhQsCAsWwIYN0Lt30GlE\nREREJCgWspssZKssZD9byP4R3tfPQnZ1JMdLc1NL5/hey3uKnNzLL8OcObpVIzMqnr84QxsMZeqH\nU9nw+4ag48SE88/3PVCeecZvIiIiIpK9WMhuBSYAS4EiQFz4pZ1Av0iOGckqGyJyEkdv1fjnP6FT\np6DTSCT61etHyQIlueuNu4KOEjO6dPG9JG67DT79NOg0IiIiIpLB+gDdXbwbBRxJsv8joHokB1RB\nQiQd9OsHBw7oVo3MLG+uvIy6YhQLvljAmh/XBB0nZjz8MFStCtdfD7t2BZ1GRERERDJQeWB9CvsP\nAvkjOaAKEiJR9sor8OyzMGmSbwQomVenGp24sOSFDFw+EOd0pxpA3ry+n8TWrX62hP5YRERERLKN\n74ALU9jfHPgykgOmuSBhxptmFElhfyEz3owkhEhWsWMH9OwJrVpB585Bp5FTlcNy8FDTh1j942oW\nfrUw6Dgxo1Il30diwQI/Y0JEREREsoUJwFQLWTv86pt1LGTDgNHA2EgOGMkMiUZA7hT25wEujSSE\nSFbRv79fFlG3amQdTSo0oXml5gxZMYSEIwlBx4kZ117r/3sfOBBWrw46jYiIiIikNxfvpgNDgJFA\nPmAucCtwh4t3z0VyzFQXJMyoYUaN8NNzjz4PbzWBrsBPkYQQyQoWL4aZM/2tGmXKBJ1Gomlsk7Fs\n3LGRxz96POgoMeXBB6FePWjbFn7Sv/4iIiIiWZ6Ld3NcvKsMFABKunhX1sW7pyI9XlpmSHyCb2Dh\ngDfDz49uHwPDgRGRBhHJzH75xd9P36aNX4lAspbqJapz8wU3E1oZ4o8DfwQdJ2bkygUvvAA5c8K/\n/uUbuYqIiIhI1mQhe9NCVgTAxbt9Lt5tC+8vZCGLqH1DWgoS5YGKhO8VCT8/upUBCjnH05GEEMnM\nnPPFiLg4mD5dt2pkVSMaj2D/4f2MeW9M0FFiSokSsGgRfP65X+pWTS5FREREsqxGRLl9Q87UDnSO\n78MPtTKHSBLTpsHSpfDqq1C8eNBpJL2UKVSGAZcM4KHVD3HbxbdRrnC5oCPFjNq1fTGuUyeoVQv6\n9g06kYiIiIhEi4WsRpKn51rISiZ5HodfZSOiG3hTXZA4JpBRGWgMnEmyAoVzum1Dso8vv4QBA+D2\n26Fly6DTSHobVH8Qj3/8OMPfGs7Ma2YGHSemdOwI69fDnXfC+efDFVcEnUhEREREouQTfOuGo+0b\nktsP9InkwGkuSJjRHZgGbAd+CYc6yqE+EpJNJCT4b4TPPhvGRrTIjWQ2BU8rSKhRiNtevY1+dftR\ns1TNoCPFlDFj4LPP4IYb4MMPoXz5oBOJiIiISBSUx7du2Ihv3/BrktcSgG0u3h2J5MCRzJAYDgxz\njgcjOaFIVhEf7y++PvgA8uULOo1klG61ujH5g8kMWj6I5Tctx9Q05E85c8Jzz8HFF/sml6tWQf78\nQacSERERkVPh4l26tW+IpCBRFHgh2kFEMpN33vFLHj7wgL9nXrKPnDly8mCTB7n6uat5fcPrtKjc\nIuhIMeX00+Hll/1yoF27wrx5avQqIiIikpVYyM4FziJZg0sX7xan9ViRFCReAK4CHovgvSKZ3h9/\nwE03waWXwqBBQaeRILSu0prL/3E5A5cPpGnFpuTMEVE7nizr/PNh1ixo2xYuvBCGDg06kYiIiIic\nKgtZBWAhUB3fruHo105H2zjEpfWYkUy52ADcb8YMMwaY0TfpFsHxRDKV3r1h505/wRWX5r9ykhWY\nGeOuGscXv37BM+ufCTpOTLr2Whg+HO6+G5YsCTqNiIiIiETBZOA7/OIW+4DzgMuAj/BLgqZZJAWJ\nHsAe4HKgN9A/ydYvkhAimcVzz8Hs2fDoo/CPfwSdRoJ0UemL6FSjE/e8dQ97EvYEHScmhUJw9dVw\n443wySdBpxERERGRU3QJcK+Ld9uBRCDRxbv3gLuAKZEcMM0FCecof4KtQiQhRDKDH3+EXr38xVWH\nDkGnkVgw6opR7Dywk7GrtMxKSnLk8AW8atWgVSvYvDnoRCIiIiJyCuKA3eHH24HS4cffA1UjOWDU\nu2SKZEWHDkH79lCokJ8doSZ9AnBW4bPoX68/41aP46ddPwUdJyblzw+vvOJX4PjnP2H37pO/R0RE\nRERi0v8BF4QffwAMtpA1AO7FLwmaZhEVJMwoa8ZtZowxY0LSLZLjicS6YcP88p7z50PRokGnkVgy\ntOFQ8ufOzz1v3RN0lJhVsiS8+ip89x20aweHDwedSEREREQiMJK/agj3AuWBd4GWwB2RHDDNreHN\nuBJYjK+AnIOvkpyN77C5LpIQIrHslVfgoYdg/Hi45JKg00isKZynMKFGIXov7c0dde/ggpIXnPxN\n2dD558OCBdCyJfTtC1OnaqaRiIiISGbi4t2yJI83AOdYyE4Hdrh4547/zuOLZIbEaGCcc1QHDgBt\ngXLASvySoCJZxqZN0KWLb8zXv3/QaSRWda/VnSrFqjBw+UBcZP8WZwtNm8Jjj8G0aTBB8+lERERE\nMhUL2dMWsoJJ97l49zuQz0L2dCTHjKQgUQ2YFX58GMjrHHvwUzaGRBJCJBYlJPjp5YULwzPP6Ntc\nOb5ccbkY23QsKzau4PUNrwcdJ6Z17Qp33QWDBsFLLwWdRkRERETSoAuQN4X9eYHOkRwwzbdsAHuB\n3OHHW4CKwH/Dz8+IJIRILBo8GNavh1Wr1DdCTq51ldY0OrsRA5cPpGnFpuTMEck/r9nDyJGwcSN0\n6gRvvw116gSdSERERESOx0JWCN+iwYCCFrIDSV6Ow/eQ2BbJsSOZIfE+0DD8eCkw3oxhwNPh10Qy\nvRdfhMmTfd+Iiy8OOo1kBmbGuKbj+OLXL3h6fUQz1rKNHDlgxgyoWRNat/bNLkVEREQkZu0Efgcc\n8A2wI8m2HV8LmBrJgSMpSNyJX+IDIB54A2gHbAK6RhJCJJZ8+y38+99w3XXQu3fQaSQzqV26Np1q\ndOLet+5l90Gtb3kiefLAokVQsCA0bw7bIqqpi4iIiEgGaAxciZ8hcR1wRZKtIXCWi3ejIjlwmucU\nO/fX+qLOsRfoFcmJRWLRgQNw/fVQvDhMn66+EZJ2o64YxYIvFvDQ6ocY0XhE0HFiWvHisGwZNGwI\nLVrAW29BoUJBpxIRERGRpFy8WwlgISsP/BDpihopSfMMCTM2mlEshf1FzP4qVohkRnfeCV98AS+8\n4JtZiqTVWYXPon+9/oxbPY6fdv0UdJyYV7GiL0ps3Aht2sD+/UEnEhEREZHjqAY0OPrEQna7hewT\nC9lcC1lEXfciuWXjbHzjiuROA8pEEiKtzGyomSWa2YRk+0eY2c9mts/MlptZpWSvn2ZmU81su5nt\nNrMFZnZmRmSW2Ddvnl+OcPJkf2+7SKSGNhxKgdwFGPbmsKCjZAo1asCSJbB2rV/Z5tChoBOJiIiI\nxD4zu93MvjOz/Wb2vpmlqvudmTUws0Nmti6Np3wIKARgIasOTMD3lSwffpxmqS5ImNHGjDbhp82O\nPg9v/wLuwfeRSFfhP+QewKfJ9g8Beodfq4NfDWSZmeVOMmwS0ApoC1wGlAZeTO/MEvvWr/fLEXbs\nCD16BJ1GMrtCpxXi/sb3M/PTmXz404dBx8kUGjTwy4C+/rr/u5iYGHQiERERkdhlZu2A8fi+jjXx\n18fLzOyEK1+aWWFgJrAigtOWB74IP24LvOLi3d3A7UCLCI6XphkSi8Kbw3+ARUm254CmwIBIQqSW\nmRUAZgPd8J0+k7oDuN85t8Q593/4dVBLA9eE31sI+DfQ3zm30jm3HrgFaGBmWnQuG9u2Da6+GqpV\ngyeeUN8IiY5utbpRo0QN+r7el0Snq+vUaN4cnn0WZs+G/v0hencnioiIiGQ5/YHHnXOznHNf4Xs7\n7sNf857IY8AcIlshMwHIF37cBPhP+PHvhGdOpFWqCxLOkcM5cgA/AGcefR7eTnOOqs6xJJIQaTAV\neMU592bSnWZWHiiJX/EjnNftwq8Gckl410X4Jp5Jx3yN/zyXINlSQgK0bet/LloE+fKd/D0iqRGX\nI47JzSfz/ub3mfv53KDjZBrt2sGjj8KUKXD//UGnEREREYk9ZpYLqM2x17YOP+vhuNe2ZnYLfpZD\nKMJTvwdMsJDdg78r4dXw/irA5kgOmOYeEs5R3jm2J91nRpFITp4WZnYjcCFwVwovl8TP3NiabP/W\n8GsAJYCEcKHieGMkG3HOL+u5di0sXAjlygWdSLKaRmc34rpzr2PIiiHsSdgTdJxMo1cvGDUK4uPh\nkUeCTiMiIiISc87A93U80fXvMcysMvAA0NG5iKfv9gYO45f+vNXFu6Md3FsAr0dywDQv+2nGEGCT\nc8wPP38BaGvGFqClc8f2dogGMyuL7//QxDmndmcSFY8+Ck8+CU8/DZdojoykk4eaPkS1qdUY894Y\nRl4xMug4mcZdd8Fvv0GfPlC0qO/vIiIiIiJpZ2Y58LdpxDvnvj26O63HcfHuB+CfKezvH2m2NBck\n8PemdAQwoyn+3pHmwA34rptXRRrmBGoDxYF1Zn/e4R8HXGZmvYFz8H+gJTi2SlQCWB9+/AuQ28wK\nJZslUSL82nGNHz+e+fPnH7Ovffv2tG/fPsKPI0F780244w7o1w9uuSXoNJKVnV3kbAbVH8TYVWPp\nWrMr5YuWDzpSpmAG48bBjh3QpQvkzOlv5xARERHJSubNm8e8efOO2bd580nvftgOHMFfyyZ1vGvb\ngvgWBhea2dTwvhyAmVkCcJVz7u205LaQvQp0c/FuS1re97fjuDR2DTNjP1DFOX40YzKQxzl6mlEF\n+MA5Ilp/9MTntPzAP5LtngF8CYxxzn1pZj8DDznnJobfUwhfnOjsnHsh/PxX4Ebn3MLwmKrhY9Rz\nzq1N4bwdgDmzZ8+mo76eyzI2boSLL4batWHpUn+hI5Ke9ibspeojValbti4v3qCFfdLiyBG4+Wa/\nLO+cOSpKiIiISNY3Z84cOnXqBP72ihSbkZnZ+8AHzrk7ws8N3x9xinPuoWRjDaiW7BC3A43xq2Vs\ncs7tT0tGC9lu4AIX7zam5X3JRXIptgMoB/yInxkx/Ggm/KyFqHPO7eWv5UX8ycz2Ar85574M75oE\nDDezDfjlR+/HN9Z4OXyMXWb2FDDBzHYAu4EpwKqUihGSNe3eDW3awOmnw3PPqRghGSN/7vyMbTqW\nji915M3v3uSK8lcEHSnTiIuDGTP846N1YRUlRERERJgAzDCzj4G1+FU38uG/uMfMRgOlnXNdwg0v\n/7+9+w6PolrjOP59U+lFOgqCDRFBKWIDFTuo1wKI2EEQRcVesIVYrooNUUBUrCgWBEVA9Nqw0hFR\nFJVmoQlIr0nO/eNMyGZJIFlIJuX3uc95dubM7Oy7nBt3591Tou+nlwObI+6nQxHL7dgo4A0zfgOq\nAR8G9c2B3/dUYHmQrWuHc66/mZUDhgJVgK+A9s65rRGn3Yjv2jISSMZPvHFN4YQrYcvIgEsugT/+\ngMmTfVJCpLB0PbQrg6YO4oYJNzCj1wwS4pQNy6vMpISZkhIiIiIiAM65t82sOnAffqjG98Bpzrl/\nglNq4zsSFJRFwG7P7xjLN+Ib8T0Q6gG3OUfm1PF1gMG7G1BeOed2+InROdcP6LeT52wBrguKlDIp\nKTBmjC+NozssiRQwM+Op05+i9fOteX7681x9xNVhh1SsxMfDSy/57Qsv9I9KSoiIiEhp5pwbTC73\n4M65nc6U55xLJfblP3Ep7tBYnxsp3wkJ59gGPJZD/ZN7IiCRgvDCC/DAA/Dww3DmDvPCihSOVnVb\n0e3wbtz9+d10ObQLe5VVN538UFJCREREJDyWaguBF4GXgxU3dltMfYbNOBA/AUZN/Oyc2znHfXsg\nLpE9ZuxYuOoq6N0bbrst7GiktHvwpAd5Z8479PuiHwPbDww7nGInMylhpqSEiIiISCEbAFwO3Gup\n9jkwDBjtUtyWWC8Yt+tTsjOjJ35livuATsC5EeWcWAMRKQiTJsH558NZZ8HAgf4mRiRMtSvU5p7j\n7mHw1MH8tPynsMMpluLj4cUX4eKLfVIialVmERERESkALsUNcCnucKA1PifwNLDEUu0ZS7UWsVwz\n3wkJ/KoadzlHbec43DmaR5SYghApCL/+6odntGgBb7zhb2JEioI+R/ahYdWGXD/hevK79LJ40UmJ\nF18MOyIRERGR0sGluBkuxfUB6uLnoegBTLVU+95Srbul5v1n4FgSElWBd2J4nkihWboUTjsNatb0\nk1iWLRt2RCJZkhOSeer0p/h0wae8+eObYYdTbGUmJXr1giuugP79w45IREREpOSzVEu0VDsfGAM8\nDkzDJyXeBf4LvJ7Xa8Uyh8Q7wKnAszE8V6TArV0LHTrA1q0wcaKW95SiqcOBHejYuCM3fnQj7Q9s\nT4NYP0gAACAASURBVJUyVcIOqViKj4dBg6BGDbj9dlixAh55RMOzRERERPa0YFhGN6ArkAG8Ctzo\nUtwvEeeMBqbm9ZqxJCR+B+434yhgNlFrjzqHZmmT0GzdCh07wrx58PXXUL9+2BGJ5O6p05+i8aDG\n9P2kL0POHBJ2OMWWGaSmQrVqcP31Pinx3HOQENO0zSIiIiKSi6nA/4CrgfdcituWwzkLgDx3AY7l\n69qVwHrg+KBEcqCEhIQjIwO6d4cvv4SPPoKmTcOOSGTn9q60Nw+e+CB9JvTh0sMu5eh6R4cdUrHW\np49PSlx+OaxaBW++CWXKhB2ViIiISImxn0txi3Z2gktxG/C9KPIk33NIOEfDnZT98ns9kT3ljjv8\n5JWvvQYnnBB2NCJ50/uI3rSq24peY3uxLT2nJLPkx0UXwfvvw8cfw+mnw5o1YUckIiIiUjLsKhkR\ni93q0GqGATiHpomXUD3wADz6KAwY4Jf5FCku4uPiGXrmUI54/ggGTBrArcfeGnZIxV6HDvC///lV\ndtq1gwkT/AS3IiIiIpI/lmr/Qt7u912Ky/fsfbGssoEZl5oxG9gEbDLjBzMuieVaIrvr4Yfhnnt8\nUuL668OORiT/WtRpQZ/WfUj5IoWFqxeGHU6JcOyxflLbJUv89vz5YUckIiIiUizdANwYlAeCuo+A\nfkH5KKi7P5aL5zshYcZNwBBgPHB+UCYAz5pxYyxBiMTq8cehb19ISYG77go7GpHY3dfuPqqVq8a1\n46/FOXU62xOaNYNvvvHbrVv7iW5FREREJO9cinslswDHAve6FNfVpbiBQekK3MuO80vmSSw9JK4D\nrnaO251jTFBuA3oDfWIJQiQWTz0Ft9ziExEpKWFHI7J7KiZXZODpAxn32zhG/Twq7HBKjP32g0mT\n4NBD4aST4NVXw45IREREpNg6Dd8ZIdoE4ORYLhhLQqIO8G0O9d8Gx0QK3KBBcMMNcNttcP/9ftk/\nkeLunIPP4T+N/kOfCX1Yu2Vt2OGUGNWq+UkuL74YLrsM7r7br8ojIiIiIvmyEjg7h/qzg2P5Fsuk\nlr/jh2n8N6q+C/BbLEGI5MfQoXDttXDTTX7+CCUjpKQwM55u/zSHDDqEuz+7m4HttYrynpKUBC+8\nAAcfDLffDnPnwiuvQLlyYUcmIiIiUmykAC9Yqp0ATA7qjgROB3rGcsFYekikAPeZMcGMe4IyIai/\nN5YgRPJq2DC46iro0wcee0zJCCl56leuT+oJqTwz5Rmm/j017HBKFDO49VYYNQrGj/fLAy9ZEnZU\nIiIiIsWDS3Ev4+eRWAucF5S1QJvgWL7lu4eEc7xrxpH4WTbPCap/Blo7x8xYghDJi5dfhp49oXdv\nv7ynkhFSUl1/1PW89sNr9Brbiyk9p5AQt1srNEuUc86Br76Cs86CI4+EDz6Aww4LOyoRERGRos+l\nuMnARXvqejEt++kc053jYudoGZSLlYyQgjRsGHTv7hMSTz+tZISUbAlxCTx31nPMWjaLR75+JOxw\nSqQWLWDKFKhRwy8LOnp02BGJiIiIFB+WamUs1SpFlliuE8uynx3MOC2H+tPMaB9LECI7078/9Ojh\nh2oMGQJxMaXRRIqX1nu35o5j76DfxH7MWDIj7HBKpL33hi+/hNNPh/PO83NLpKWFHZWIiIhI0WSp\nVs5S7RlLteXABuDfqJJvsdzaPZxbfDs5JpJvzvnx3rffDvfe61fWUDJCSpOUE1I4tOahXDL6Ejan\nbQ47nBKpfHl45x0/J83jj8Mpp8CyZWFHJSIiIlIkPQqcCFwNbAF64OeSXAxcGssFY7m9OxCYm0P9\nL8ABsQQhEi0tDa64wt8kPPUUpKZqmIaUPknxSbx27mv8vup37v7s7rDDKbHM4Oab4dNP4eefoXlz\n+OabsKMSERERKXLOAnq7FPcukAZ85VLcA8CdxDivRCwJiTXAfjnUH4DvtiGyWzZvhk6d4LXXYPhw\nv6KGSGl1aM1DefDEB3niuyeYuHBi2OGUaMcfDzNnwgEH+BU4BgzwPbVEREREBIC9gPnB9tpgH+Br\n4LhYLhhLQuJ9YIAZ+2dWmHEA8DgwJpYgRDKtWePHc3/8Mbz/Ply0x+ZvFSm+bjzqRtrUb8Pl71/O\n2i1rww6nRKtTx/eUuOEGuPFGuOACWLcu7KhEREREioT5QMNg+xfg/GD7LGB1LBeMJSFxG74nxC9m\nLDBjAX7Zz5XALbEEIQJ+3Ha7djBrFvzvf9ChQ9gRiRQN8XHxvHLOK6zYuIKbProp7HBKvMREePRR\nGDkSPvwQWreGOXPCjkpEREQkdC8BmYulPwxcY6m2GXgSP79EvuU7IeEca4BjgDOAwfieESc5x4nO\nxZYVEVm4ENq0gSVLYOJEvwyfiGRpWLUhA04bwLCZw/hg7gdhh1MqdOwIU6dCfDy0auVX+dEQDhER\nESmtXIp70qW4gcH2J8DBwIVAc5finorlmgkxBeJwwMdBEdktX3/tl9yrVMlPJLdfTjOUiAjdm3fn\nvbnv0eODHvy4z4/UKF8j7JBKvEaNYMoUuOUW6N0bxo+HYcOgZs2wIxMREREJj6VaGZfiFgGLduc6\nWkRRQvXyy3DiiXDIITBpkpIRIjtjZjx/1vOkZ6TTa2wvnH6uLxTlysHgwfDBBzB5MjRt6hMTIiIi\nIqWJpVq8pdo9lmp/A+st1fYL6u+3VLsilmsqISGhSE/3vzh26waXX+4nsaxePeyoRIq+2hVqM/TM\noYz+ZTTDfxgedjilyplnwuzZfvjGGWfANdfAxo1hRyUiIiJSaO4CLsfPK7k1ov5HoEcsF1RCQgrd\n2rXwn//Ak0/CU0/B0KGQlBR2VCLFR8dDOnJJs0u49sNrWbR6t3rJST7VqgVjx8Izz8CLL/rkxMyZ\nYUclIiIiUiguBa50Ke51ID2ifhZ+Pol8U0JCCtW8eXD00X6uiA8/hD59wCzsqESKn4HtB1K1TFU6\nv9OZLWlbwg6nVDHzvSOmT4fkZDjySHjkEUhLCzsyERERkQK1N/B7DvVxQGIsF4wpIWHG/mY8YMYI\nM2oGde3NaBLL9aR0+OILv3ze1q1+vohTTw07IpHiq0qZKow8fyQ/LPuB6ydcH3Y4pVLm3Dc33AB9\n+/pk6w8/hB2ViIiISIGZA7TNob4TEFOf0XwnJMw4HpgNHAmcB1QIDh0GpMYShJRszsGzz8Ipp0Dz\n5n5SuINj6tAjIpFa1W3FoA6DGDp9KC/NfCnscEql5GTo3x++/RY2bYKWLeHuu2Hz5rAjExEREdnj\n7gOesVS7HZ9LOM9S7Xn83BL3xXLBWHpIPAzc7RynkH0ii8+Ao2IJQkqutWvhwgvh6qvhqqv8MI29\n9go7KpGS44oWV9CzRU+uHnc1M5bMCDucUuuoo2DGDJ+M6N/fJ1+//jrsqERERET2HJfi3gfOAk4G\nNuCTEI2Bs1yK+18s10yI4TlNgQtzqF8OaJ0E2W7GDOjSBZYtg7fegvPPDzsikZJpYPuBzFw6k45v\nd2Raz2lUK1ct7JBKpaQkSEmBTp2gRw9o2xZ694aHHoJKlcKOTkRERGT3uRT3FXDKnrpeLD0kVgN1\ncqhvDvy9e+FISeCcn4H+6KP9l/CZM5WMEClIZRLKMLLzSNZtWcdFoy4iPSN910+SAtOkie8d8dRT\n8Morfn/cuLCjEhEREdk9lmrzLdV2+OXLUq2Kpdr8WK4ZS0LiTeARM2oDDogz41jgMeDVWILYFTPr\na2ZTzGytmS0zs9FmdlAO591nZovNbKOZ/c/MDog6nmxmg8xshZmtM7ORZlazIGIurVav9r8OXncd\n9Orlx1Xvv3/YUYmUfPtW2ZcRHUfw8byPuW9iTEP4ZA+Kj/erCP34o09InHkmnHceLFgQdmQiIiJS\nUpjZNWa2wMw2mdkkMztiJ+eea2Yfm9lyM1tjZt+aWX6XGWgAxOdQn4xfgSPfYhmycScwCPgzCGZO\n8PgG8EAsQeRBW+BpYBo+5oeAj82ssXNuE4CZ3Q5ci18bdWEQy0fBOZlzXQwA2gMdgbXB+3iXnGcK\nlXyaMsUP0Vi9GkaNgnPPDTsikdLllP1P4YETH+Cuz+7iiL2P4MyDzgw7pFKvQQM/d85bb8Ett0Dj\nxnDbbXDHHVCuXNjRiYiISHFlZl2Ax4ErgSnAjfj734OccytyeMpxwMdAX/yoh+7AB2bW2jk3a6ev\nlWr/idg9zVJtTcR+PHAS/h48/+/DORfL8zCjPnAofpWNmc7xW0wXium1rTp+zorjnHNfB3WLgUed\nc08G+5WAZcBlzrm3g/1/gAucc6ODcxoBPwNHOeem5PA6FwKvDx8+nIsuuqgw3lqxlJEBAwb4L9jN\nm/sv3g0ahB2VSOmU4TI4961z+XLRl0zrOY3991IXpaJiwwb473/hscegVi14/HHfo8ws7MhERESk\nKHn99de5+OKLAS5yzr2R0zlmNgmY7Jy7Ptg3fKeBgc65/nl5HTP7EXjTObfTjgWWahnBpgOiv7ls\nwycjbnYpbmxeXjdSLEM2fCSOP5xjvHO8XZjJiEAV/D/GKgAzawjUBj7Nis+tBSYDRwdVrfC9KyLP\nmQv8EXGO5NO8edCuHdx8sx+m8dVXSkaIhCnO4nj1nFepXq465719Hhu3bQw7JAmULw8PPghz5sDh\nh/u5dU46yQ/rEBEREckrM0sEWpL93tYBn5DHe9sggVGR4J56Z1yKi3MpLg5/71wzcz8oyS7FNYol\nGQExJCTMMDM6mzHYjJFmjIossQSRv9c3ww+9+No5NyeozpzPYlnU6cuCYwC1gK1BoiK3cySPMjLg\n6aehWTP44w/47DP/a19SUtiRiUjlMpUZdf4ofl/1O5eOvlSTXBYx++8PY8bA+PHw998+OXH99bBq\nl18HRERERAC/umU8O7//3ZVbgfLA23l9UZfiGrqUHIeDxCyWHhIDgNeAhsB6YE1UKWiDgUOACwrh\ntSQH8+bBiSf6Cdsuvxxmz/a9JESk6GhaqylvnPcGo38ZzXUfXkesw/Ok4LRv7//7+dBD8OKLPlHx\n8MOwUZ1aREREpAAFUxPcA3TOZb6J7Oen2tGWamdG1V1qqbbAUm25pdpzlmrJscQSy6SWlwDnOcf4\nWF5wd5jZM0AHoK1zbknEoaX4sSy1yJ4lqgXMjDgnycwqRfWSqBUcy9Xjjz/OW2+9la2ua9eudO3a\nNab3UVxlZMCgQX6uiJo1fa8IJSJEiq6zDz6boWcOpecHPaldoTb3Hn9v2CFJlKQkuPVWuPRSP5zj\n3nt977OUFOjWDRITw45QRERECtKIESMYMWJEtrq//vprV09bAaTj72Uj7fLe1swuAJ4DOjnnPs9j\nmPcCXwBjASzVmgLDgJfxczLeCiwG+uXxetvFkpBYA8S0xujuCJIRZwPHO+f+iDzmnFtgZkvxs3v+\nEJxfCTgSv5IGwHQgLTgnclLL+sB3O3vtm2++udRPajlvHlxxBUycCL17wyOPQIUKYUclIrvSo0UP\nlm9Yzl2f3UXN8jW5qtVVYYckOahVCwYOhBtu8EmJq67yw+AefBA6dtTElyIiIiVVTj90R0xqmSPn\n3DYzm46/tx0D26c2OAkYmNvzzKwr8ALQxTk3IR9hHo7vUZHpAmCyS3E9ASzV/gRSiSEhEcuQjX5A\nihllY3huTMxsMHARcCGwwcxqBaVMxGkDgLvN7Cwzawq8CvwFvA/bJ7kcBjxhZieYWUvgReCbnFbY\nEG/rVp98aNYMFi3yvSIGDVIyQqQ46dumL31a96H3uN68O+fdsMORndhvPxg+HGbO9EM4OneG1q3h\n0093/VwREREpVZ4AeprZpWZ2MPAsUA7fawEze8jMXsk8ORim8QpwMzA14p66Uh5eqyrZRyIcD3wY\nsT8VqBfLm4glIfF2ENByM2abMSOyxBJEHlwFVMJ3E1kcUc7PPCFY2uRpYCh+dY2yQHvn3NaI69yI\n72YyMuJaHQso5mLv44+haVO46y7o1UtzRYgUV2bGk6c/SZdDu3DhqAv5fEFee+dJWA47zE96+cUX\nkJAAJ5/s5+75/HPQdCAiIiLinHsbuAW4Dz9NQTPgNOfcP8EptcmeJOiJnwhzENnvqQfk4eWW4eeQ\nxFItCWgBTIo4XhG//Ge+xTJk4xX8EiPDg8AK/KuRcy5PiRPnXD920k3EObcFuC4okouFC+Gmm2D0\naDjhBBg1Cpo0CTsqEdkdcRbHK+e8wsqNKzn7zbOZePlEmtdpHnZYsgvHHw/ffutX5UhN9UmJY4+F\nu++G007TUA4REZHSzDk3GL/oQ07HukXt785Py+OBhy3VbgfOATYCX0UcbwbMi+XCsfSQOAM41zmu\ndo5+zpEaWWIJQoqGzZvh/vuhcWOYPBlGjPBDNJSMECkZkuKTePf8d2lUvRHtX2/PvFUxfW5IITOD\ns8+G6dNh3DhIT/crdLRuDe+/7yccFhERESlA9+DnY5yI72nR06VkG4nQHfg4lgvHkpD4E1i7y7Ok\nWBk71ice7rvPL+c5dy5ccIF+fRMpaSomV2T8heOplFyJU4efytL1O52IWYoQM+jQwfeY+OQTKF8e\nzjkHDj8c3nrLJypERERE9jSX4la4FHccfuqGqi7FjY46pTPE1jkhloTEzUB/MxrE8oJS9DzxBJx1\nlp9MbfZsraAhUtLVKF+Djy/5mE3bNnHKa6ewZN2SXT9JigwzOOkkP7/El19CnTo+gdy4sZ90eMOG\nsCMUERGRksiluDUuxe3wE4hLcauiekzkWSwJieFAO2CeGevMWBVZYglCwtW1K4wc6SexPPjgsKMR\nkcLQoEoDPr30U/7d9C9tXmrD/H8LfTVn2QPatoWPPvLD7A4/3Pdwq1cP7rgDdr2EuYiIiEi4YklI\n3ABciR8nci1+5YrIIsVMnTpa516kNGpcozFfd/+aeIunzYttmL1sdtghSYxat4a334b586F7dxgy\nBBo08AnnKVrYWkRERIqofCcknOOVnZWCCFJERApGgyoN+KrbV9SqUIvjXj6O7/78LuyQZDfsuy88\n9pjvHfHEEz4ZceSRfmWOkSNhW0wLcomIiIgUjDwlJMyoFLm9s1JwoYqISEGoVaEWX1z2BU1rNuXk\n107m43kxTZIsRUjFin74xq+/+iWcExKgc2eoXx/uussv7ywiIiIStrz2kPjXjJrB9mrg3xxKZr2I\niBQzlctUZsLFE2jXoB1nvnEm7/z0TtghyR4QH+9X4pg4EWbN8sPznnnGT2J8+uk+WaFeEyIiIhKW\nvCYkToTtE1a2C/ajS2a9iIgUQ+USyzG6y2g6N+lMl5FdeH7682GHJHtQs2Y+GbF4MbzwAqxeDeed\n54d53H23ek2IiIhI4UvIy0nOMdGMe814zDkmFnRQIiISjsT4RF479zX2KrMXV469khUbV3BHmzsw\nzXpbYpQv7ye+7N4dvv8ennsOBg6E//4X2rWDSy7xPSkqVgw7UhERESnp8jOpZQpQoaACERGRoiHO\n4hjYfiApx6dw52d3csG7F7Buy7qww5ICcPjhMHiw7zUxbBhkZEC3blCrFlx4IXz4IaSlhR2liIiI\nlFT5SUjo5zERkVLCzOh3Qj/e7vQ2438bzxHPH8FPy38KOywpIBUq+ETE55/DokVwzz2+90SHDrDP\nPnDTTTBzJjgXdqQiIiJSkuR32U99FRERKUU6N+nMtJ7TSIhLoPULrXn9h9fDDkkKWP360Lcv/PQT\nTJsGF1wAr78OLVpA48Y+WTFrlpITIiIisvvym5D41YxVOysFEqWIiISmUfVGTO4xmY6NO3Lx6Ivp\nPa43W9K2hB2WFDAzaNkSBgyAv/+G8ePhmGNg0CA/1KNRI7jzTpgxQ8kJERERiU2eJrWMkAKsKYhA\nRESk6CqfVJ5XznmFNvXbcN2H1zFt8TTe6fwO+1bZN+zQpBAkJED79r48+6wf2vHOOzB0KDz0kF9G\ntFMnv2rHEUdAXH5/7hAREZFSKb8JiTedY3mBRCIiIkWamXFlyytpWaclnd7pRPOhzRl+3nA6HNgh\n7NCkECUlwWmn+TJkCHzxBYwcCS++CP37+wkxzzgDzjwTTjnFz08hIiIikpP8/IahDpkiIkLLui2Z\nfuV0jql3DGe8cQa9PujF6s2rww5LQpCY6JMOQ4fCkiXw5Zdw2WXw3Xe+t0S1anD66X6Yx6JFYUcr\nIiIiRY1W2RARkXzbq+xejOk6hqfbP82IH0dw8DMH89aPb+E0mUCplZAAbdvCI4/AnDnw++++x0Ra\nGtxwAzRoAE2bws03w4QJsHFj2BGLiIhI2PKckHCOOA3XEBGRTHEWx7Wtr2XONXM4pt4xXPDuBZzx\nxhksXL0w7NCkCNh/f7j+evjkE1ixAt5+G1q1grfe8nNRVK0KJ50EDz/sJ8bMyAg7YhERESlsmnZK\nRER2yz6V9mFUl1G81+U9Zi+fTZPBTXjs28dIy0gLOzQpIipXhs6d4aWX4M8/fQ+K/v2hbFl44AG/\nmketWn6J0WefhZ9/1sodIiIipYESEiIiskecffDZzOk9h54tenL7J7fT6rlWTP17athhSRFjBo0b\n+94TY8fCqlUwcSL06gULF8K118Ihh0Dt2j6J8cwzMHu2elCIiIiUREpIiIjIHlMxuSIDTh/A5B6T\nMTOOfOFIur3fjfn/zg87NCmikpLguON8T4lJk2D1avjoI+jRw0+UedNN0KwZ1KgB55wDjz4KX38N\nmzaFHbmIiIjsrvwu+ykiIrJLreq2YmrPqQyZOoQHv3qQ4T8Mp9vh3bir7V3sW2XfsMOTIqxCBTj1\nVF/AJx4mTfK9KCZOhH79/ISYiYlw+OFw9NFZpX593wNDREREigclJEREpEAkxCVw3ZHXcUWLKxgy\ndQiPfPMIL3//Mj1a9ODOtneyT6V9wg5RioGyZaFdO1/Ar9oxe7ZfWvS772DcOBg40B+rUwdat/aT\nZ7Zq5eemqFEjvNhFRERk55SQEBGRAlUusRw3H3MzV7W6ikFTB9H/m/4MmzmMXi170bdNX+pUrBN2\niFKMJCRA8+a+9O7t65Yv970ovvsOpk6Fxx/3Qz/A95rITE60agUtWkD16uHFLyIiIlmUkBARkUJR\nPqk8tx17G1e3upqnpzzNY98+xvMznqfb4d24utXVNK3VNOwQpZiqWRP+8x9fwK/QMX8+TJsG06f7\nx0cegbVr/fG6deGww7JKs2Zw0EE+2SEiIiKFRx+9IiJSqComV+TOtndyzRHXMHDyQIZMG8KQaUM4\ntt6xXNXqKjod0okyCWXCDlOKMTPYf39funTxdRkZMG8ezJwJs2b5Mnw4PPywP16mDDRp4pMTTZr4\nlT6aNIF69TQvhYiISEFRQkJEREJRuUxl7jn+Hu5ocwdj5o7h2enPcsnoS7h+wvV0O7wbV7a8koOq\nHRR2mFJCxMXBgQf6cv75WfUrV8IPP2QlKWbNgrfe8hNngp9k85BDskqTJtCoEey7r3pUiIiI7C59\nlIqISKgS4xPpeEhHOh7Skd9W/sZz05/jpe9f4vHvHuekhifRo0UPzjjwDComVww7VCmBqlXLPmkm\n+N4UixbBnDm+/PSTL++8Axs2+HMSE+GAA/xQj4MO8kmKzO2aNdWrQkREJC+UkBARkSLjwGoH8uip\nj3L/iffz7px3GTJtCF3f7UpyfDKnHXAaHRt35KyDzqJq2aphhyolWFwcNGzoyxlnZNVnZMBff8Hc\nufDrr77MnQsjR8LChX7uCoCKFWG//bKGjUSWevXUs0JERCSTPhJFRKTIKZNQhouaXcRFzS5i4eqF\njPp5FO/+/C6XvXcZCXEJnNjwRDo27sjZjc6mVoVaYYcrpURcnF+1o359OOWU7Mc2b/ZzVMyd6x8z\ny8iR8McfkJ7uz0tI8M9v0MAP+4h+3GcfJSxERKT00EeeiIgUaQ2qNOCmo2/ipqNvYvG6xbz3y3u8\n+/O79B7Xm6vHXc2x9Y7l5P1Opl2Ddhy5z5EkxSeFHbKUQpmTYjZpsuOxbdv8EJD5832SYsEC36Pi\np59g3Di/bGmm+HjYe2+fmKhXL+fHWrX8eSIiIsWdEhIiIlJs1K1Yl95H9Kb3Eb1ZsXEFY+aO4YNf\nP+DJSU+S8kUKZRPK0qZ+G9o1aEe7hu1oVbcVCXH6qJNwZc43ccABOR/ftMn3oli40CcuFi3yQ0P+\n/BNmzPCPmzdnnZ+QALVr++VL69Txj5GlTh1/vHp1JS5ERKRo07c0EREplqqXq0735t3p3rw76Rnp\nzFo2i88WfMbnCz/nv1//lzs/u5MKSRVoW78tR+1zFC3rtKRl3ZbUrlA77NBFsilb1k+K2ahRzsed\ng1WrspIUf/0Fixf7smQJfPed347saQF+iEmNGr5HRe3a/jFzu2ZNfyyylC1b8O9VREQkkhISIiJS\n7MXHxdOiTgta1GnBLcfcwrb0bUxfMp3PF3zOF4u+4KnJT7Fq0yoA9q64Ny3rtvQJijotaVW3leah\nkCLNzK8GUq0aHHZY7udt3QrLlvnkxNKlfjvycf58+PZbv79+/Y7Pr1Ahe4Ii8zWrVfO9LSL3q1WD\nvfZSEkNERHaPEhIiIlLiJMYnctQ+R3HUPkfRt21fnHMsXL2Q6UumM23xNKYvmc6ASQP4d/O/ANQo\nV4PGNRpzcLWDObi6L41rNKZ+5frEWVzI70Ykb5KS/DwT9ert+txNm+Cff3xZvjxrO7L8/jtMngwr\nV/oeGhkZO14nOdknJqpW9SVyu2pVqFwZqlTxJXq7UiVN4CkiUtqVyo8BM7sGuAWoDcwCrnPOTQ03\nKikMI0aMoGvXrmGHIXuQ2rRkKaj2NDMaVm1Iw6oN6XRIJwCccyxYvYDpi6cz5585/LLyFyb/PZlX\nf3iVzWl+wH7ZhLIcVO0gDqx2IA0qN2DfKvvSoEoD9q28L/tW2ZdKyZX2eKwlif4+i66yZbNWDMmL\njAwYNmwEJ5zQlZUrfZLi3399WbUq++O8eX57zRpYvRo2bsz9uuXL+8REpUpZSYrM7YoVd14qkErW\ncwAAFE1JREFUVMhekjSfbb7pb7RkUXuWPvm9rzWzE4DHgSbAH8CDzrlXCiHUXJW6hISZdcE3wpXA\nFOBG4CMzO8g5tyLU4KTA6T/UJY/atGQpzPY0M/aruh/7Vd0vW32Gy+CPNX/wy4pf+GXFL/z8z8/M\n+3ce3y/9nkWrF7EtY9v2c6uWqUqDKg2oV7kedSrUoU6FOtSuUJs6FbO2a1WoVWpX/tDfZ8kRFwcf\nfDCCnj27cuCB+Xvutm1ZyYnVq/32v//C2rVZZc2a7I+LF8O6ddlLWtrOXychISs5Ub68fyxXzm9n\nPkZulyuXVcqW3XG7bNnspUwZ3yPELPZ/x6JGf6Mli9qzdMnvfa2ZNQDGAoOBC4GTgRfMbLFz7n+F\nFXe0UpeQwDfUUOfcqwBmdhVwBtAd6B9mYCIiEr44i6NBlQY0qNKA0w84PduxDJfB0vVLWbh6IYtW\nL2LRmkUsXL2QP9f+yZS/p7B0/VKWbVhGhsvet32vsntRvVx1qpWtRrVy1fxj5Ha5alQpU4XKyZWp\nXKby9sfk+GSsJN39SKmUmOjnoKhePfZrOAdbtvjExPr1WUmKDRv8/vr1OW9v3Oi3N2zww1A2bMhe\nt2mTL3ll5hMTZcpkJSqSk7Pqokty8o4luj4padePSUn+3zFzO7JOK6mIlFr5va+9GpjvnLst2J9r\nZm2C6yghURjMLBFoCfw3s84558zsE+Do0AITEZFiIc7iqFuxLnUr1uWYesfkeE56RjorNq5gyfol\nLF2/lCXr/OPKTSt92biSX1f+un37383/7pDAyJQYl7g9QVExuSIVkipQPrG8f0wqT4VE/1g+sTzl\nk8pTNqEsZRPLUjahLGUSymzfznxMTkgmOT55+2NSfBLJCclaGlWKvMhEQI0ae/baGRk+2bFxoy+b\nNmXf3rw5+2P09pYtfj+ybNrke4FkHo8umzf7x61bdz/+uLisZEViYs7bCQlZ+4mJWfszZkCnTn4/\nsy7yMXo7s8TH5163s8fMErkfuR0Xl/286BJ9PHI/Lq5k9V4R2ZkY72uPAj6JqvsIeLJAgsyj0vYN\npDoQDyyLql8G5LLYloiISN7Fx8VTq0KtPK/ckeEyWL15NWs2r2HNljXZHtduWbt9e93WdWzYtoH1\nW9ezfut6lm1Yxvqt69mw1ddt3LaRTWmb2Jqe/zucOIvbnqBIjE/0j3GJO2wnxiWSEJdAYrx/zKnE\nWzzxcfHEWzyz6s+i97je2erjLG6H7TiL276fUzEs+75ZtvrM/cw6M8v3I7DLOmCH7cxz8rMfWbd9\nP+pOak8fj5aXnjfR11hdbjVT/84amry7vXd2FeOekO8YE4CKvpTBl4LknB+KsnWrH9qyLQ22BduZ\ndWlpwbFt2fczn5eWlr1kHovcT0/f8bxNabC12mr+zphB+iZfl56eVdLSIC14zIiqjz4vPb2A/6Hy\nIS4uKzkRH+8fM+sy6yP3I+ssDuJs53VmO14n8nUzt6PP3dk+BhbxPMy/puVQMl8jp2PTF6/mwptn\nbE/MRL5eTs/JsS6znh3rM/+a8loffR2itjP3ox+j/2yjjzWoU4luZx+Qj/9XlEix3NfWzuX8SmaW\n7JzbsmdDzJvSlpDIrwoA33zzTdhxyB7y119/8frrr4cdhuxBatOSRe2ZJY44qgT/24EBSUGJkkEG\n29K3sTVj6/ayLX0bW51/THNpbHPbSEtPI82lkZbh97dlbCPdpZOenk5aWhrpGemku3TSMtL8o0sj\nw2WQ7tLZ5DaR7tK372c+Zm5nln9W/8P4z8aT7tJxzvlHHBkuY/tjBhk453Auq97hyCADHFnHM58X\nbEsIKkPr+1qHHYXsDgMSgwLQBibRMsSA9ryMoABs29mJxYkLCsDOkj8tYcT6ktWeOan4+WEkrb81\n7DAKVMT9Z4Uw4ygM5lzp+VAPurZsBDo658ZE1L8MVHbOnRt1/jPANYUapIiIiIiIiAgMcs5dG12Z\n3/va4NhEYLpz7qaIusuBJ51zVQsg9jwpVT0knHPbzGw6cBIwBsB8X76TgIE5PGVA8PgDsL5QghQR\nEREREZHSrALQjKz70WxiuK8F+A5oH1V3alAfmlLVQwLAzM4HXgauImt5lE7Awc65f0IMTURERERE\nRGSXdnVfa2YPAXWdc5cF5zcAZuOX/XwRn7wYAHRwzkVPdlloSlUPCQDn3NtmVh24D6gFfA+cpmSE\niIiIiIiIFAd5uK+tDdSLOH+hmZ2BX1WjD/AXcEWYyQgohT0kRERERERERCR8cWEHICIiIiIiIiKl\njxISgJndaWbfmNkGM1uVw/FmZvaGmf1hZhvN7Ccz65PLeV+a2SYzW2RmJXs9miJsV20anFPPzMYF\n5yw1s/5mFhd1jtq0CDKzA83sPTP7x8zWmNlXZnZC1Dm7bF8pWszsDDObFPx3dpWZjYo6rjYtZsws\nycy+N7MMM2sWdUztWQyY2b5m9oKZzQ/+Nn8zs37BDO+R56k9ixEzu8bMFgTfbyaZ2RFhxyS7ZmZ9\nzWyKma01s2VmNtrMDsrhvPvMbHHwN/s/MzsgjHglf8zsjuDz8omo+hLdnvqg8BKBt4EhuRxvCSwD\nLgIOAR4EHjKz3pknmFlF4CNgAdACuBXoZ2Y9CjBuyd1O2zT4kjQeP4/KUcBlwOX4MViZ56hNi65x\nQDxwAr5tZgFjzawm5K19pWgxs47Aq8AwoClwDPBGxHG1afHUHz9GNdv4ULVnsXIwYEBP/HegG/ET\nqD2YeYLas3gxsy7A40AK0Bz/GfpRMBZdira2wNPAkcDJ+O+7H5tZ2cwTzOx24FrgSqA1sAHfvkmF\nH67kVZAUvBL/9xhZX/Lb0zmnEhT8B+iqPJ77DPBJxP7VwAogIaLuIWBO2O+rNJfc2hS/5M02oHpE\nXS/g38w2VJsWzQJUAzKAYyPqKgR1J+a1fVWKTsEnl/4ELt/JOWrTYlaCNvsJf0ObATRTe5aMAtwC\n/K72LJ4FmAQ8FbFv+MThbWHHppLvtqwe/Pe1TUTdYuDGiP1KwCbg/LDjVcm1HSsAc4ETgc+BJ0pT\ne6qHROwqA5FDAY4CvnTOpUXUfQQ0MrPKhRqZ5MVRwGzn3IqIuo/w7dok4hy1aRHjnFsJ/AJcambl\nzCwBnzxaBkwPTstL+0rR0QKoC2BmM4JuiePNLLKt1KbFiJnVAp4DLsZ/cYqm9izeqrDjdyC1ZzEQ\nDLVpCXyaWef8Xc4nwNFhxSUxq4LvgbYKwMwa4ldWiGzftcBk1L5F2SDgA+fcZ5GVpaU9lZCIgZkd\nA5wPDI2oro2/IYq0LOKYFC15aS+1adF1Cv4mdh3+Zud64HTn3JrguNqueNkP/wtdCr6L9xn4X1a/\nMLMqwTlq0+LlJWCwc25mLsfVnsVUMHb5WuDZiGq1Z/FRHd8rLaf2UlsVI2ZmwADga+fcnKC6Nj5B\nofYtJszsAuBwoG8Oh0tFe5bYhISZPRRMCpJbSc9pEpg8XPdQ4D2gn3Pu012dL3tOQbWpFA35bN/B\n+P8YHwscgf+bHBv8KitFRD7aNPOz6AHn3HvBTWw3/Idw59DegGST1/Y0P+lzBeCRzKeGGLbkIpbP\nVDPbG/gQeMs592I4kYtIYDB+XpcLwg5EYmNm++CTShc557aFHU9YEsIOoAA9hv+FZmfm5+eCZnYI\nvkvbs865h6IOLwWib4ZqRRyT3bcn23Qp/kY2UnR7qU0LV57a18xOAjoAVZxzG4L6a83sVPycIf3J\nW/tKwcvr32zdYPvnzErn3FYzmw/UD6rUpuHLS3suANrhu5Ju8T/gbTfNzF53znVD7VkU5Osz1czq\nAp/hf43tFXWe2rP4WAGkk/P3G7VVMWFmz+C/C7V1zi2JOLQUnwSuRfZf1WsBufVYk/C0BGoAMyzr\nAzMeOM7MriVrUuES3Z4lNiERjDNfuaeuF4xl/hR4yTl3bw6nfAc8YGbxzrn0oO5UYG5EN3LZDXu4\nTb8D7jSz6hFjXk8F1gBzIs5RmxaSvLZvMJO0w0/iFCmDrF/a89K+UsDy0abTgS1AI+DboC4RaAAs\nCk5Tm4YsH+15HXBXRFVd/HwC5wNTgjq1Z8jy85ka9Iz4DJgKdM/hFLVnMeGc2xb8N/ckYAxs7/p/\nEjAwzNgkb4JkxNnA8c65PyKPOecWmNlSfHv+EJxfCb8qx6DCjlV26RP8ymKRXsb/QPOwc25+aWjP\nEpuQyA8zqwfsBewLxJvZYcGh351zG4JhGp/huykOiOgWnh7xwfsGcC/wopk9gv8/Vx/82HYpZLtq\nU+Bj/Jek14LldOoA9wPPRHSZUpsWTd8Bq4FXzex+/BwSV+JvXscF5+SlfaWIcM6tM7NngVQz+wuf\nhLgNn3h6JzhNbVpMOOf+itw3sw34X3jmO+cWB9Vqz2Ii6BnxBb73y21Azcwf8pxzmb/YqT2LlyeA\nl4PExBT8Uq7l8DdCUoSZ2WCgK/AfYEPEPcka59zmYHsAcLeZ/Q4sxP8t/gW8X8jhyi4E9yTZkrbB\nZ+ZK51xmr9GS355hL/NRFAq+y2J6DuW44HhKLsfnR13nUGAisBH4A7gl7PdWWsuu2jQ4px4wFliP\n7wb1CBCnNi36BT+h5YfAP/jkxDfAqVHn7LJ9VYpOwXdR7A8sCdr0I6Cx2rT4F3xiOJ2IZT/VnsWn\n4IfCRX+WZuB/lFF7FtMC9Mbf3GzCJ/pbhR2TSp7aLSOX77eXRp3XD79c5Mbg8/SAsGNXyXMbf0bE\nsp+loT0teJMiIiIiIiIiIoWmxK6yISIiIiIiIiJFlxISIiIiIiIiIlLolJAQERERERERkUKnhISI\niIiIiIiIFDolJERERERERESk0CkhISIiIiIiIiKFTgkJERERERERESl0SkiIiIiIiIiISKFTQkJE\nRERERERECp0SEiIiIiWMmX1lZv3DjmNPMrP9zSwjKFMK6TXvj3jN3oXxmiIiIqWJEhIiIiJFgJmN\nMbMPcznWNrgpPjTGa/9ZFG6ozSzZzFaa2U25HE81s7/MzHK5hAOOA07bjRjqmNk2Mzsvl+OvmNmk\nYPchoDawJNbXExERkdwpISEiIlI0DANONrO6ORzrBkx1zv1YyDHtUc65LcAb+PeTk0uBl51zLpfj\nBqxyzv27GzEsASYA3Xe4uFkFoCPwQnDuRufcciAj1tcTERGR3CkhISIiUjSMBVYAl0dWmll5oBPB\nTXJQ187MpprZZjP728weyK1XgZl9BewNPB30stga1Fc3sxFBj4QNZjbLzDpHPbdicM76zF4W0cNB\ngl4PTwRxrDezb82s7U7e5zDgEDNrHfVaJwP1gZd2+S+V/Xmvmdk7Zna3mS0zs1Vm1tfM4s3s8WD/\nDzO7JCqGU82sTtTlLsD3wngzPzGIiIhIbJSQEBERKQKcc+nAq0QlJIDz8Z/XbwKYWT1gHPA10Ay4\nBrgK6JvLpf+DH3LQFz/8YO+gviwwGWgPHIpPeLxuZs0jnjsQOALoAJwOnAo0jbr+s0BLfNKkKTAa\nmGBmDXJ5n98D37NjD4XLgS+dc/NyeR87cypQDWgD3Ao8iE/wLA3ifwF43sxqBeePBVYBl+UQw0jn\n3PoYYhAREZF8UkJCRESk6HgROMDMjououxx41zm3Lti/BpjnnLvROferc+49IBW4JacLBsMbMoD1\nzrnlzrl/gvo/nXMDnHOznXMLnHNPA58CnQHMrDJwEXCjc+5L59xPQSyJmdc2s4bAxUAn59x3wXUe\nBaawY2Il0jCgi5mVCa5TCTg3qI/F8uDf4zfn3DBgHpDonHs0SHA8GPwbHBu89zSikj9mdhBwzG7E\nICIiIvmkhISIiEgR4ZybC3xL0HvAzA4A2hIxXANoHJwT6RugspnVzutrBUMaUszsh2CiyXXAifhh\nEwD7A/HA1Ij4VgO/R1ymaXDOPDNbl1nwN/b77+Tl3wCS8b0qAC4EtgIj8xp/lOi5NZYBsyPiTsf3\niKgZcc6LwEFm1ibY7w785pz7OsYYREREJJ8Swg5AREREshkGDDSza/CTP/7unPuqAF6nL3A1cD0w\nB9gADAKS8nGNCvhEwuE5HMt12INzbrWZjca/v+H4ngojnHOb8/HakbZFv0Quddt/iHHO/WJm3wHd\nzOxbfE+PgTG+voiIiMRAPSRERESKlrfxwwsuAi5hxyEEP+N7IERqA6x2zi3N5Zpb8T0ZIh0DjHbO\nveWcmw0sBA6MOD4PSMfPwQCAmVUFDog4ZwZ+CEcN59z8qLJ852+TYcDxZnYG0DqH91kYhuGHqHTC\n9554NYQYRERESi0lJERERIoQ59wGfFLiIfwklK9EnfIMsJ+ZDTCzRmZ2LnAv8NhOLrsQf/Nf18z2\nCup+A04zs6PM7BDgeaB6RBxr8L0XnjCz483sUPwN/DZ8bwOcc78Esb5uZmebWQMzax2scnHqLt7n\nZ8AifBJgtnNu+i7+aQrCW8HjEODDnSR0REREpAAoISEiIlL0DAOqABOib5Kdc3/hV704Br9axTP4\nG+qHI0+Lut49+N4P8/ErTwDcB/wAfAx8gk8OjIl6Xh/8BJXjgAnAZ/g5JCKHVlwCvA48AfwCvAu0\nAP7Mw/t8KXifee0dEf2+8nPeDnURyZ/8xCAiIiJ7iDmX1892ERERKc3MrALwN3Ctc+61Qn7t/fG9\nOg51zs0p5Nf+E3jIOTe4MF9XRESkpFMPCREREcmRmbUwsy5mtp+ZtcKvjrGNHXtSFBYHTDGzLwrj\nxczs7mDVkDqF8XoiIiKljXpIiIiISI7MrCXwHH64xxZgOnBTYfdQCGJJIGtJ0s3OucWF8JpVgMw5\nN/5xzq0r6NcUEREpTZSQEBEREREREZFCpyEbIiIiIiIiIlLolJAQERERERERkUKnhISIiIiIiIiI\nFDolJERERERERESk0CkhISIiIiIiIiKFTgkJERERERERESl0SkiIiIiIiIiISKFTQkJERERERERE\nCp0SEiIiIiIiIiJS6P4PtQVj9kKk5kQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115a3c650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ih = Ih(nest.GetDefaults('ht_neuron'))\n",
    "\n",
    "V = np.linspace(-110, 30, 100)\n",
    "plt.plot(V, ih.tau_m(V));\n",
    "ax = plt.gca();\n",
    "ax.set_xlabel('Voltage V [mV]');\n",
    "ax.set_ylabel('Time constant tau_m [ms]', color='b');\n",
    "ax2 = ax.twinx()\n",
    "ax2.plot(V, ih.m_inf(V), 'g');\n",
    "ax2.set_ylabel('Steady-state m_h^inf', color='g');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The time constant is extremely long, up to 1s, for relevant voltages where $I_h$ is perceptible. We thus need long test runs.\n",
    "- Curves are in good agreement with Fig 5 of Huguenard and McCormick, *J Neurophysiol* 68:1373, 1992, cited in [HT05]. I_h data there was from guinea pig slices at 35.5 C and needed no temperature adjustment.\n",
    "\n",
    "We now run a voltage clamp experiment starting from the equilibrium value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ih = Ih(nest.GetDefaults('ht_neuron'))\n",
    "nr, cr = voltage_clamp(ih, [(500, -65.), (500, -80.), (500, -100.), (500, -90.), (500, -55.)]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ksWwT6sZfTJNjkzm1XjLtmjXgxHrHEhcXvp81M0AtDhMVfsQNt/c0oTfGGGOi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\nkSQpK3Pue+1DbnvnPiY0fQqsjA04nAv7DuPCQ3bTnPOSVfR+FZH6iBd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bnpeczBmf7oDj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      "text/plain": [
       "<matplotlib.figure.Figure at 0x118957110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(nr.times, nr.I_h, label='NEST');\n",
    "plt.plot(cr.times, cr.I_h, label='Control');\n",
    "plt.legend(loc='upper left');\n",
    "plt.xlabel('Time [ms]');\n",
    "plt.ylabel('I_h [mV]');\n",
    "plt.title('I_h current')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(nr.times, (nr.I_h-cr.I_h)/np.abs(cr.I_h));\n",
    "plt.title('Relative I_h error')\n",
    "plt.xlabel('Time [ms]');\n",
    "plt.ylabel('Rel. error (NEST-Control)/|Control|');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Agreement is very good\n",
    "- Note that currents have units of $mV$ due to choice of dimensionless conductances."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### I_T Channel\n",
    "\n",
    "The corrected equations used for the $I_T$ channel in NEST are\n",
    "\\begin{align}\n",
    "I_T &= g_{\\text{peak}, T} m_T^2(V, t) h_T(V,t) (V-E_T) \\\\\n",
    "m_T^{\\infty}(V) &=  \\frac{1}{1+\\exp\\left(-\\frac{V+59\\text{mV}}{6.2\\text{mV}}\\right)}\\\\\n",
    "\\tau_{m,T}(V) &= 0.13\\text{ms} \n",
    "  + \\frac{0.22\\text{ms}}{\\exp\\left(-\\frac{V  + 132\\text{mV}}{16.7\\text{mV}}\\right) + \\exp\\left(\\frac{V +  16.8\\text{mV}}{18.2\\text{mV}}\\right)} \\\\ \n",
    "h_T^{\\infty}(V) &=  \\frac{1}{1+\\exp\\left(\\frac{V+83\\text{mV}}{4\\text{mV}}\\right)}\\\\\n",
    "\\tau_{h,T}(V) &= 8.2\\text{ms} +  \\frac{56.6\\text{ms} +  0.27\\text{ms} \\exp\\left(\\frac{V   + 115.2\\text{mV}}{5\\text{mV}}\\right)}{1 +   \\exp\\left(\\frac{V  + 86\\text{mV}}{3.2\\text{mV}}\\right)}\n",
    "\\end{align}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nest.ResetKernel()\n",
    "class IT(Channel):\n",
    "    \n",
    "    nest_g = 'g_peak_T'\n",
    "    nest_I = 'I_T'\n",
    "    \n",
    "    def __init__(self, ht_params):\n",
    "        self.hp = ht_params\n",
    "        \n",
    "    def tau_m(self, V):\n",
    "        return 0.13 + 0.22/(np.exp(-(V+132)/16.7) + np.exp((V+16.8)/18.2))\n",
    "\n",
    "    def tau_h(self, V):\n",
    "        return 8.2 + (56.6 + 0.27 * np.exp((V+115.2)/5.0)) /(1 + np.exp((V+86.0)/3.2))\n",
    "\n",
    "    def m_inf(self, V):\n",
    "        return 1/(1+np.exp(-(V+59.0)/6.2))\n",
    "\n",
    "    def h_inf(self, V):\n",
    "        return 1/(1+np.exp((V+83.0)/4.0))\n",
    "\n",
    "    def compute_I(self, t, V, m0, h0, D0):\n",
    "        self.m = si.odeint(self.dm, m0, t, args=(V,))\n",
    "        self.h = si.odeint(self.dh, h0, t, args=(V,))\n",
    "        return - self.hp['g_peak_T'] * self.m**2 * self.h * (V - self.hp['E_rev_T'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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oKltrT3sRk+Ri1sLMmc7U+19/hXvuga++guuvdzuyzJPPk4937nqHUwmnMC4U\ndzAGnnoKHnzQ+W9ctarfQxDxq192/0Kzj5pRLaIaX7f/mtACGc6Ri4iIZLr777+fDRs28PHHH/Pq\nq69StGhRjDEUK1aMt956ixtuuIG7776bfPny8dVXX9GjRw+stTz22GPp9u3rvy0XLlzItGnT6NGj\nB4ULF2bMmDG0atWKuLg4wsNzd/J98uTJTJ48+YK2nTt3pvfYfiABiLioPQLYk8ozzwCLrbWjk85/\nM8b0ABYaY5611l48u8EvvNnwrhVwMAP3GeDrjHZqrb2gepwx5i5gs7V2YVJTb2CotXZG0vWOOFM/\n7iEp2SB5l7UwaxYMGQKLF0NUlFMQ8MYb3Y4sa9Qvm9rSKf9o08ZJ1rz0EnzwgauhiGSprYe2cuek\nO7mu2HV80/4bChco7HZIIiKSx91www3UrFmTjz/+mLvvvpty5cqdv/bDDz9QoECB8+c9evTgjjvu\nYPTo0RlKJPhq/fr1rFu3jsjISACioqKoXr06kydPpkePHlk2bnbQtm1b2rZte0HbxIkT6dChQ6rP\nWGvPGGNWAE2ALwGMk8VpAoxJ5bFg4OIP0BNxPuh3rXR8RhMJ24EfrCVDJeONYQtwxttgkopPtAde\nSjovj7NWJHkxiqPGmGU4xSiUSBD69YPAQPjuO7jttpyxE0NOFRgIffrAggWQmOjUThDJbfbH7+f2\nibdTKLAQM9vNVBJBRCSX273bOVITFARVqqTdx9q1cPLkpe0lSzpHVkueRDh69ChnzpyhUaNGfP/9\n9/z9998ULpw1f5fddttt55MIAFWrViU0NJQtWzIyMT3PGg1MSEoo/ISzi0MwMAHAGPM8UMpa2ynp\n/q+At40x3YHvgFLAy8Aya21qsxiyXIYSCdZS3ptOreUG38LhXqAIzj6Z4CQRLF4Uo5C8xRiYMweK\nFlUCwV+efNJJJojkRvFn4mk5uSWHThxiycNLtMWjiEgeMG4cxMSkfr1KFfj997T7eOABJ5lwseho\nGDz4ssLLkMWLFxMdHc2PP/5IfHz8+XZjDEeOHMmyRELZsmUvaQsPD+fQoUNZMl5uYK2daowpBgzB\nWdKwCmhurf0r6ZYSQNlk939gjAkBeuJ84H4Y54P2Z/wa+EW8WdqQKmMIs5bM2IOkC/CNm5kVyXmK\naRc2v1LCRnKrhMQE2n3WjtV7VzOv0zwqXqG6viIiecGjj0LLlqlfDwpKv49PPkl9RkJW27JlC02b\nNqVy5cp28vDSAAAgAElEQVS8/PLLlC1blvz58zNz5kxeeeUVEhMT0+0jISHBp7EDAgJSbNfuEmmz\n1r4BvJHKtc4ptL0OvJ7VcXnD60SCMfQHtlnLlKTzqcD9xrAHuNNaVvsSiDGmHNAUp/bBOXtw1n1E\ncOGshAjgl/T6HDVqFFOmTLmgLaW1LJI9/f47TJkC114LaSw1EhG5bNZanvjmCWZsmMH0f0+nbum6\nbockIiJ+khnLD9Jb+pBZUiqK+NVXX3H69Gm++uorSpcufb59zpw5l9wbHh7O4cMXfv575swZdqe1\ntkMkBb7MSOiOU8cAY7gNuA24A2gNvAg08zGWLjjJgvOFGq21W40xe3CKT6xxxjShQD0ykJHp27cv\n7du39zEcccPGjU7y4OOPnURCWBj07+92VCKS2z2/6HneXP4m4+8az7+u/Zfb4YiIiKSoUKFCABw+\nfPh8scVzswKSzzw4cuQIEyZMuOT5q6++mh9++OGCtnHjxvk8I0HyLl8SCSWAHUnftwCmWsv3xrAN\nWOZLEEmVKh8CJlhrL5578wrwnDFmE872j0OBncB0X8aS7GfXLoiNdRIIK1dCSAjcfTc8/zw0awbJ\nasdICsYuG0v+gPw8WvtRt0MRyZEmrJrAs3OfJSYqhodrPux2OCIiIqmqVasW1loGDhzIv//9bwID\nA2nUqBGBgYG0aNGCRx99lL///pvx48cTERHBnj0Xrhjv2rUr3bt3p1WrVtx2222sXr2a77//nuLF\ni7v0E0lO5UvN9UP8U/zhdmB20vcGSHmRTPqaJvX5/sUXrLUjgbHAOJxERUHgDmvtxVtgSA61cKFT\nhObqq+HTT2HfPiexcNddSiJkxPr963l69tPsPebKFrIiOdq3m76l65dd6VazG/9t9F+3wxEREUlT\n7dq1GTZsGGvWrKFz5860a9eOsLAwPvvsMzweD/369ePtt9+me/fu9OrV65Lnu3XrxjPPPMPChQt5\n6qmn2L59O7NmzaJQoUIpLptIizEmxWdSa5fcxXhbCMMYXsOZibAR+D8g0lqOGcO/gaetpWbmh+kd\nY0w7YGJsbKyWNuQA8fHOVoIhIW5HkjMdiD/ANWOv4d5K9/Lu3e+6HY5IjrFqzyoavNeAxuUbM63N\nNPJ5MqX+sIiIuGjlypXUqlWLFStWULOm629LJJdJ7/dr4sSJdHCKu7W31k7ye4B+5MuMhD7Aa8Ba\n4DZrOZbUXpJUKk9K3mEtbNvmVK7t1w+iomDAgLSfCQ5WEuFyFA0uyrDGw3h/1fv8/OfPfh///feh\ndWu/DytyWQ7EH+DeKfdyXbHr+LjVx0oiiIiIiHjB6385WcsZnP0rL25/OVMikhznp5/gm2+crz//\nDH8l7YBarhzUrQuVK7sbX17wSK1HeGv5Wzwz5xnmdLy0Qm9WCglxEkcrVkCtWn4dWsQnCYkJtJvW\njmOnjzG/03yCA4PdDklERCTb2Ls37eWyBQsWJDQ01E/RSHbl00cwxlAKaABcyUWzGqxlTCbEJTnI\nV1/Bm29CnTrw2GPO1zp1ICLC7cjyjnyefETfEk2rT1qxZMcSbip7k9/Gvu8+qFABXnzR2W1DJLt7\nbu5zzN4ym1kPzuKqsKvcDkdERCRbKVmyJMYYUloCb4yhU6dOvPfeey5EJtmJ14kEY3gIp/DhaeAA\nkPw3zIISCTmNtc4sgm3bYPt252vy46OPIK0lZs8+C0OGgGqquOveyvdyffHrGfrDUL5p/43fxg0I\ngL594YknnJ02ypf329AiXvt07aeMWDyCF297kcblG7sdjoiISLYze/bsNK+XKlXKT5FIdubLjISh\nwBDgeWu5eKtGyUbOnoVTpyBpu9kUHTsGV14JJ07801a4sPNmMDISmjSBggXTHicoKFPClcvkMR6e\na/Qcg+cP5sjJIxQJKuK3sR96CKKj4eWXYYxSiZJN/b7vdx764iHaXN+GvvX7uh2OiIhIttS4sRLt\nkj5fEgnBwMdKImQPU6bAokVw8CAcOOAc574/csSZdv7ZZ6k/HxICI0dCmTJO4uCqqyAsTLMLcqrW\n17fmgSoPEODxdSdW3wQHQ8+ezvKG6GgoWtSvw4uk6/DJw9w75V7Kh5fn3ZbvalsqERERkcvgSyLh\nXeABYEQmxyI+WLEC5s933rgVLQply/7zfdGicM016ffx+ONZHqb4icd4wKX3Rz17wgsvOPUynnvO\nnRhEUpJoE3nw8wf5K/4vfu72M4XypzFNS0RERETS5UsiYQAwwxhuB34FziS/aC3/yYzAJGNGjnQO\nEbcVLw4dO8K8eUokSPYydMFQZm6YyYx2M6h4RUW3wxERERHJ8XxNJDQH/kg6v7jYoojkUaNGpV2T\nQ8TfZmyYweAFgxkSNYQ7r7nT7XBEREREcgVfEgl9gS7WMiGTYxGRHC4kxO0IRP6x8cBGOkzrQMvr\nWvJso2fdDkdEREQk1/D48MwpYHFmByIiIpJZTp09RZtP23BloSv58J4PnfohIiIiIpIpfPmX1avA\nE5kdiIhkDWu14kjynoFzBvLbvt+Y0mqKX7dCFRERycmioqK49dZbfX7+o48+onLlyuTPn58rrrgi\nEyOT7MaXpQ11gcbG0AL4nUuLLd6XGYGJyOXbdHATD3zyAFNaTeHaote6HY6IX3y76VtG/ziaUc1G\n8X8l/8/tcERERHIMYwwej2+z+P744w86d+7MnXfeyYABAwgODs7k6CQ78SWRcBiYltmBiEjmKxNa\nhr3H9vK/hf9jwj0T3A5HJMvtPbaXTl90ovnVzXnyxifdDkdERCRHmTVrls/Pzp8/H2str776KuXL\nl8/EqCQ78jqRYC2dsyIQEcl8QfmC6H9zf/p+35dBtwyiQngFt0MSyTKJNpHO052/oibcM0F1EURE\nRLyUL58vnzM79u7dC0BoaGhmhSPZWLb4V5YxppQx5iNjzH5jTLwxZrUxpuZF9wwxxuxKuj7LGKPN\nwEUyoFutbhQLLsbzC5/3+9iTJsGECX4fVvKoscvG8s2mb3j/7vcpEVLC7XBEREQy3eDBg/F4PGzc\nuJEOHToQFhbGlVdeyaBBgwDYsWMH99xzD0WKFKFkyZKMHj3aq/6joqJo3Ljx+fMFCxbg8Xj45JNP\nGD58OGXLlqVgwYI0bdqUzZs3n7+vfPnyDB48GIDixYvj8XgYMmTI5f/Akm1lKJFgDCuNITyjnRrD\nImMonbF7TRjOLhCngOZAZZwtJg8lu6c/8DjwCE6NhuPAd8aY/BmNSSSvCg4M5qmbnmLC6glsP7zd\nr2PPnw8DB8Lp034dVvKgVXtW8fTsp+ldrzd3XnOn2+GIiIhkCWMMAG3atAHghRde4MYbb2T48OG8\n8sorNGvWjDJlyjBy5EiuueYa+vXrx6JFi7zu/2IjRoxg+vTp9OvXj4EDB/Ljjz/SoUOH89dfffVV\n7r33XgDGjRtHbGws992n0nm5WUbnrtQAqhvDQS/uL5DBe58B4qy1XZO1Xfxupzcw1Fo7A8AY0xHY\nC9wDTM3gOCJ5Vvfa3Xlh8QuMWDSCN1u86bdxn3wS3nkHpkyBBx/027CSx8SfiaftZ22pXKwyLzR9\nwe1wREREstyNN97IG2+8AUC3bt2IjIzkqaeeYsSIETz11FMA/Pvf/6ZUqVK89957NGjQ4LLGO3Xq\nFKtXryYgIACAsLAwnnzySdauXUuVKlVo2bIlv/zyC1988QX333+/dmzIA7xZBDMHSDlFdSlv9pu7\nC/jWGDMVuAX4E3jDWjsewBhTHiiRNL7TubVHjTHLgPookSCSrpD8ITxZ70mGLRzG0MZDKRZczC/j\nVqkCt98Oo0dDhw6QSpJb5LL0+bYP2w9vZ8UjKyiQL6M5bBEREScZvX7/+iwdo1KxSgQHZt4OBsYY\nHn744fPnHo+H2rVrM336dLp06XK+vUiRIlx33XVs2bLlssfs0qXL+SQCQMOGDbHWsmXLFqpUqXLZ\n/Yt/mBjzINAdKA/Ut9F2u4kxTwJbbbSd7k1fGU0k+FJ2c2cG76sAPAaMAobjLF0YY4w5Za39CCeJ\nYHFmICS3N+maiGTAo7Uf5Y3lb/Dr3l+5tbzv+wN76z//gWbNYMECiIry27CSR0xbN423V77NuBbj\nqFy8stvhiIhIDrN+/3pqvV0rS8dY8cgKapasmf6NXihXrtwF50WKFCEoKOiSmQBFihTh4MGMTipP\nXdmyZS84Dw93Vr0fOnQopdslGzIx5jFgCPAK8CxwLjN0GHgSyPxEgrWXLDXITB7gJ2vtf5POVxtj\nbsDJlHyUheOK5CnFgosR92QcAZ6A9G/ORE2bwg03OLMSlEiQzLTjyA66ftmV+yrfR7ea3dwOR0RE\ncqBKxSqx4pEVWT5GZks+OyCtNgBrvZksnvHxMqtv8ZsngG422n5hYswzydqXAy9525nv+3tknt3A\nuova1gHnqnPswVlSEcGFsxIigF/S6njUqFFMmTLlgra2bdvStm3by4lXJMfydxIBnOUMffrAww/D\nhg1w7bV+D0FyoUSbSKcvOlEofyHeueudVItDiYiIpCU4MDjTZwuIZFPlSfn98ymgkLedZYdEwmLg\nuovariOp4KK1dqsxZg/QBFgDYIwJBeoBr6fVcd++fWnfvn2mBywi3mnXDgYMgDffhJdfdjsayQ3e\n/PlN5m2bx+wHZ3NFQRV0EhEREUnHVpxNES5ebXA7l36wn67skEh4GVhsjBmAUzixHtAVSD5P9RXg\nOWPMJmAbMBSnBoNX6zhExB1BQfD111BZS9glE2w+uJmnZz/NY7Ufo0mFJm6HIyIiIpITjAZeNzEm\nCGfGf10TY9oCA3Def3vF9USCtXa5MeZeYATwX5xMSW9r7cfJ7hlpjAkGxgFhwELgDmutdqcXySFq\nZW0dI8kjEm0iXb7swpWFrmTkbSPdDkdERCTbSG2Zn7fL/y6+P7P6FXfZaDvexJgTwDAgGJgE7AJ6\n2+h/3ntnlPG2QIYxlMWpq7Ez6bwu0A5Yay1vextAVjDGtAMmxsbGammDiEguMmbZGHp/25t5neYR\nFRnldjgiIpLNrFy5klq1arFixQpq1lTtA8lc6f1+TZw4kQ4dOgC0t9ZO8nuAGWRiTDAQYqPtPl/7\n8PjwzCTgVgBjKAHMwtmycbgxDPI1EBERkbRsOriJZ2Y/Q886PZVEEBEREfGCiTFzTYwJA7DRNv5c\nEsHEmFATY+Z6258vSxtuAH5K+r418Ju13GwMzYC3cPamFJFsbt1f6/jfov8x/q7xFMhXwO1wRNKU\naBPpPL0zJQuXZETTEW6HIyIikqPs37+fhISEVK/nz5+f8PBwP0YkLogC8qfQHgQ09LYzXxIJgThb\nRAA0Bb5M+n49UNKH/kTEBR7jIXZNLE3LN6VTjU5uhyOSpjHLxrAobhHzO80nJH+I2+GIiIjkKHXq\n1GH79ouL9f8jKiqKuXO9/lBacgATY6olO61iYkyJZOcBOLs2/Oltv74kEn4HuhvDTOA2nAKJAKWA\nAz70JyIuuK7YddxR8Q5eXfYqHat3VMEcybY2HNjAwDkD6VW3F7dE3uJ2OCIiIjnOpEmTOHHiRKrX\nNRshV1sF2KQjpWzRCeAJbzv1JZHQH/gc6Ad8YC2rk9pb8s+SBxHJAXrX683tE29nUdwiGl7l9Ywm\nn1kL8+dDkSKgOkiSloTEBDpP70ypwqX4X5P/uR2OiIhIjlS/fn23Q5BkjDE9gaeAEsBq4Alr7c9p\n3J8fiAbaJz2zCxhirZ2QgeHK42z3uAWntuFfya6dBvbZaJv6updUeJ1IsJb5xlAMCLWWQ8kuvQ0c\n97Y/EXFPs6ubUalYJV5d9qpfEwkAffpAiRLw7bd+HVZymFeXvcrSHUtZ8NACCuUv5HY4IiIiIpfF\nGNMGGAU8gvNBfB/gO2PMtdba/ak89glQHOgMbMYpKZChjRNstD23psWXjRZS5XUiwRjmAvddlEQA\nOAh8ATTOjMBEJOsZY3ii7hM88c0TbD+8navCrvLTuPD009C+PaxaBTVq+GVYyWH+2P8Hz859lt71\nevs90SUiIiKSRfoA46y1HwIYY7oD/wK6ACMvvtkYcztOMcQK1trDSc1xvg5uYkwVoBwXFV600fbL\nlJ9ImS9ZiaiLB03iU7VHEXFXx+odKZy/MK///Lpfx23dGiIjYeQl/7sU+WdJQ9nQsgxvMtztcERE\nREQumzEmEKgFzDnXZq21wGwgtfUndwHLgf7GmJ3GmD+MMS8aY4K8GjvGVDAxZjXwGzATZxLAFzhl\nCz739mfJcCLBGKoZw7mKj1XOnScd/wc8jA/VHkXEXSH5Q+hasyvvr3qfMwln/DZuvnzQty9MnQpb\nt/ptWMkh3vj5DZbuXMp7d79HcGCw2+GIiIiIZIZiODsl7L2ofS9O7YOUVMD5wP564B6gN9AK8PZT\nwFeBrcCVQHxSf41wkhRRXvbl1YyEVcAv/FPtcVWyYwXwHDDE2wBExH39burHykdWEhgQ6Ndxu3SB\n8HAYPdqvw0o2t+PIDgbOHchjtR+jQbkGbocjIiIi4iYPkAi0s9Yut9Z+C/wH6GSMKeBFP/WBQTba\n7k/qL9FG20XAAGCMt0F5UyMh/WqPFq+rPYqI+yJCIlwZNzgYnngCRoyAQYOgeHFXwpBsxFpLj697\nEFoglOebPO92OCIiIiIpmjx5MpMnT76gbefOnek9th9IAC7+x3cEsCeVZ3YDf1prjyVrW4fz3rwM\nTvHFjAgA/k4WRyngD2A7cF0G+zgvw4kEa8mSao8ikrf17AkvvACvvQYxMW5HI277ZO0nzNgwg8/b\nfE6RoCJuhyMiIiKSorZt29K2bdsL2iZOnEiHDh1SfcZae8YYswJoAnwJYIwxSeepzQpYDLQyxgRb\na+OT2q7DmVWQbuYimd+A6jjLG5YBT5sYcxpn94gtXvQD+JgUMIZrjOERY3jOGAYlP3zpT0TyrqJF\nYdIk6NbN7UjEbYdOHKLXN724r/J93FPpHrfDERERyXYGDx6Mx+Ph4MGDmdKPr5YvX87NN99MSEgI\nAQEBrFmz5rLiyWNGA92MMR2NMZWAt4BgYAKAMeZ5Y8wHye6fBBwA3jfGVDbGNMLZ3eFda+0pL8Yd\nxj/v/wfhrDhYCNyJU3fBK75s/9gNeBNnOsQenJoJ51hUJ0FEvHT33W5HINlBv1n9OHn2JGPvGOt2\nKCIiItmSMQbnA+zL78fXRMLZs2dp1aoVwcHBvPLKKwQHB3PVVf7ZQjw3sNZONcYUw3nfHIFTc7C5\ntfZc6YASQNlk9x83xtwGjAV+xkkqTAH+69W40fa7ZN9vAiqZGHMFcMhGW5v6kynz5bfnOeBZaylh\nLTWs5f+SHTW97cwYE22MSbzoWHvRPUOMMbuMMfHGmFnGmIo+xC0iItnUvK3zePeXdxl520hKFS7l\ndjgiIiK52n//+1/i4+PTvzEFmzdvJi4ujn79+tG1a1fatWtHkSJajugNa+0b1tpIa21Ba219a+3y\nZNc6W2sbX3T/Bmttc2ttiLX2Kmvt017ORsDEmPdMjCl8Qb/R9iAQbGLMe97+DL4kEsKBT3x4Li2/\n4WRjSiQd58t0G2P6A4/jrN2oCxwHvjPG5M/kGERExAUnzpzgkRmP0LBcQ7rW7Op2OCIiIrmex+Mh\nf37f3k7t3evsXKjkQY7TCSiYQntBoKO3nfmSSPgEaObDc2k5a639y1q7L+lIvuinNzDUWjvDWvsb\nzg9ZCmcPTRHJApsPbmbdX+vcDkPyiKE/DCXuSBxv3/U2HqN6viIiIuk5dOgQDz30EOHh4YSFhdGl\nSxdOnjyZ4edTqpHg8Xjo1asX06dPp2rVqgQFBXHDDTfw3XfnZ8TTuXNnoqKiMMbQqlUrPB4PjRs3\nvrh7yUZMjAk1MaYIzi4PhZPOzx3hODUS9nnbr9c1EoBNwFBjuBH4FTiT/KK13u9BCVxjjPkTOAks\nBQZYa3cYY8rjzFCY80//9qgxZhnOPphTfRhLRNLR6pNWRIZF8nmbz90ORXK5NXvX8OKSFxnUaBCV\nilVyOxwREZFsz1pL69atqVChAiNGjGDlypWMHz+eiIgInn8+Y1snp1ZrYeHChUybNo0ePXpQuHBh\nxowZQ6tWrYiLiyM8PJzu3btTpkwZhg8fTu/evalTpw4REe5sIy4ZdhinlqEFNqRw3QLR3nbqSyLh\nEeAYcEvScXEQ3iYSfgQewtnDsiQwGPjBGHMDThLBAnsvemZv0jURyQKP1HyEJ755gp1Hd1ImtIzb\n4UgulZCYQNcvu3Jd0evo36C/2+GIiIjkGLVq1eLtt98+f75//37efffdDCcSUrN+/XrWrVtHZGQk\nAFFRUVSvXp3JkyfTo0cP6tWrx8mTJxk+fDgNGzbkvvvuu6zxxC9uxZmNMBe4H0g++/80sN1G213e\ndup1IsFaynv7TNr9/VM9EvjNGPMTsB1oDazPzLFEJGPaV2tPv1n9eHflu0RHeZ2gvGybNsGePdCg\nQfr3Ss712k+vsXzXchZ3WUz+AJW9ERERd+z+eze7j+1O9XpQviCqFK+SZh9r/1rLybOXLi0oGVKS\nkoVLXnaMyRljePTRRy9oa9iwIV988QXHjh0jJCTE575vu+2280kEgKpVqxIaGsqWLVt87lPcZaPt\nAgATY8oDcb7s0JASX2YkZClr7RFjzAagIjAfJ3sSwYWzEiKAX9Lra9SoUUyZMuWCtrZt29K2bdtM\ni1ckNwotEEr7qu15Z+U7PNvoWfJ5/Pu/iqefhjVrYN06CAz069DiJ3FH4nh27rP0rNOT+mXrux2O\niIjkYeNWjCNmQUyq16sUr8LvPX5Ps48HPnmAtX+tvaQ9+pZoBkcNvtwQL1GuXLkLzsPDwwGndsLl\nJBLKli17SVt4eDiHDh3yuU/JNirjbCu5CMDEmJ5AN2At0NNGW69eZJ/eHRhDGaAlUA644GMka/mP\nL33+07cJwUkifGCt3WqM2QM0AdYkXQ8F6gGvp9dX3759ad++/eWEI5JnPVr7Ud5e+TZfb/yalte1\n9OvY0dFQowZ8+CE8/LBfhxY/sNby+NePExYUxvAmw90OR0RE8rhHaz2a5r91gvIFpdvHJw98kuqM\nhKwQEBCQYru9zA+bs6pfyRZeBPoDmBhTFRgNjMJZ+jAa6OxNZ14nEoyhCfAlsAWohLN1YyTOzIGV\n3vdnXgS+wlnOUBqIwSng+HHSLa8AzxljNgHbgKHATmC6t2OJSMbVLFmTOqXq8Nbyt/yeSKheHVq1\ngqFD4cEHwcfdiSSb+mL9F3y14Ss+a/0ZoQVC3Q5HRETyuJKFL3/5QXpLH0SygfI4sw/AqZXwlY22\nA02MqQl87W1nvuyz9TzwkrVUxdll4X6cKRILcLaG9FYZYBJOPYSPgb+AG621BwCstSOBscA4YBnO\nPpd3WGtP+zCWiHihe+3ufLvpW7Ye2ur3sQcPhrg4eO89vw8tWejvU3/zxDdPcNe1d3FvpXvdDkdE\nREQkrzgNBCd93xT4Pun7g4DXn+z4srShMnCuyMBZoKC1HDOGQTizBN70pjNrbboFC6y1g3F2cxAR\nP2pzfRsWxS0iwSb4fezrr4e2bWHYMHjoIQhKf1ah5ACD5g3i0MlDjL1jbIrbTomIiIhIllgEjDYx\nZjFQF2iT1H4tzox/r/gyI+E4/9RF2A1cnexaMR/6E5FsqlD+Qrx393tUvKKiK+NHR8Pu3ZBsdyPJ\nwVbuXsmYn8YQExXDVWFXuR2OiIhInnZxQt8Yk2KSP6V2fRiQIz2OMxGgFfCYjbZ/JrXfAXzrbWfG\n28IZxvAFMNNa3jGGl4C7gQnAfcAha2nqbRCZzRjTDpgYGxurYosiOVznzvDtt7B9u2ol5GQJiQnc\n+O6NnE44zfJuywkM0HYcIiKS+VauXEmtWrVYsWIFNWvWdDscyWXS+/2aOHEiHTp0AGhvrZ3k9wD9\nyJcZCf/BqVUAEA3MwZkWsQ1QfXURyVQxMfDVV0oi5HRv/PwGK3atYFyLcUoiiIiIiLjIxJiZJsZc\nVoVRr2skWMuWZN8fB7pfTgAiImkpV845JOf68+ifPDv3WbrX7s6NZW50OxwREZFc6+jRo5w4cSLN\neyIiIvwUjWRjjXA2MfCZL9s/bgHqWMuBi9rDgJXWUuFyAhIRkdyl97e9KZS/EP9r8j+3QxEREcnV\nevfuzQcffJDqdWMMCQn+L6ItuY8vuzZEAgEptBcASl9WNCIikqvM2DCDz9Z9xuT7JxMWFOZ2OCIi\nIrla//79efDBB90OQ7K/7cCZy+kgw4kEY2iZ7LS5MRxJdh4ANMGpkyAiuZi1VpV6JUOOnz5Oz697\n0uzqZrS5vk36D4iIiMhlqVSpEpUqVXI7DMnmbLS94XL78GZGwhfnxgUuni9zBieJ0PdyAxKR7OuN\nn99g5saZzGw30+1QJAeIWRDDvuP7mNtxrpJPIiIiIi4yMWYb8B4wwUbbuMvt7//Zu+/wKKq2gcO/\nk0oKhJ7QpfcmVZHepAoIQgQREQRs6AcWVAwBXvRVUXyxoVhAihQbgtKkioLSkd4EIpBQA4SQhOR8\nf5wsKSSb7LLJpDy317lm9uzMmWcSSTLPnpLpVRu0xk1r3ICTQEnb68TirTXVtWbpnQYkhMi5SviW\n4OfDP7M3Yq/VoYgcbnf4bt79413GtxpP5aKVrQ5HCCGEECK/mwb0AY6pULVKhaoBKlR5O9uYw8s/\nak1FrTmfvC5xokUhRB73QI0HKOlXkhnbZlgWQ0wMTJgAu3ZZFoLIQIJOYMTSEVQrVo2x9461Ohwh\nhBBCiHxPh+hpOkQ3AJoC+4HpwBkVqj5QoepuR9tzOJGgFC8pRf9krxcBF5XiX6Wo72h7Qojcw8vd\ni6ENhjJ712yux123JAY3N1iwAEaPBq0tCUFk4NNtn7I5bDMzus/Ay93L6nCEEEIIIUQiHaK36xD9\nLFAaCAWGAX+pULVThaqhKjRz41GdWbVhJDAQQCk6Ah2A+4GHgLeBTk60KYTIJYY3Gs6bm95kwd8L\neFjtj64AACAASURBVKzhY9l+fU9PmDYN7r8fFi+Gfv2yPQRhx5mrZ3h59cs83vBxWlZoaXU4Qggh\n8qn9+/dbHYLIg/LC/1cqVHkCvYHHgI7AZuBzoCwwBfN8/3BG7TiTSAgCTiXudwcWas1KpfgH2OJE\ne0KIXKRSkUp0qtyJGdtmWJJIAOjcGXr0gLFjoVs38PW1JAyRhudXPI+XuxdvdXzL6lCEEELkQ8WL\nF8fX15dBgwZZHYrIo3x9fSlevLjVYTgscfjCY0AwkADMBp7XIfpAsmO+B/7KTHvOJBIuAeUwyYT7\ngdds18UsAymEyONGNBrBgwsfZNfZXdQPsmZE09SpULs2vPMOvP66JSGIVJYfWc6CvQv4uvfXFPUp\nanU4Qggh8qHy5cuzf/9+zp8/n/HBQjihePHilC9f3uownPEXsAoYBfygQ3RcGsccB77JTGPOJBK+\nA+YpxWGgGPBLYn1D4IgT7Qkhcpke1XpQyr8Uyw4vsyyRULUqPP88vPkmDBkCufPned5xPe46Ty57\nkvYV2zOw7kCrwxFCCJGPlS9fPrc+6AmRlSrpEH3C3gE6REdhei1kyOHJFoHngQ+AfUBHrbmWWF8K\n+MiJ9lJQSr2slEpQSr2bqn6iUuq0Uuq6UmqVUqrKnV5LCOEcT3dPdo7cySstX7E0jtdeg4AAeOkl\nS8MQwOQNkzl99TQfdfsIlbk5eoQQQgghRDbJKIngKId7JGhNHPBOGvXv3WkwSqkmwBPArlT1LwFP\nA4OBf4DJwAqlVE2tdeydXlcI4biSfiWtDoGCBU2PhPffh6go8POzOqL86e+Iv3n797cZ32o81YpV\nszocIYQQQggBqFB1CcjUOmc6RDs0LtWZoQ0oRVWgLVCSVL0atGaic20qf2AOZvmJ8aneHg1M0lov\nTTx2MBAO9AIWOnM9IUTe8MgjMGgQuMsMLZZI0AmMXDqSykUq81IL6RoihBBCCJGDPJdsvxhmfsMV\nwB+JdfcAnYFJjjbscCJBKYYDHwPngbOkzHBocC6RAHwI/KS1XqOUupVIUEpVxKwU8euti2h9RSm1\nBXPjkkgQIh9zc2aAlnCZz7d/zqZTm1j76Fq8PbytDkcIIYQQQiTSIXqWbV+Fqm+B13WI/iDZIf9T\noeppzJKPDo0wcKZHwmvAq1rzXyfOTZNSagDQAGicxttBmARFeKr68MT3hBBCWCD8Wjgvrn6RIQ2G\n0OauNlaHI4QQQggh0tcZSKv76HLgTUcbc+azvCLAIifOS5NSqiwwDRiodZpLUAghhMiBxqwcg7ty\n5+2Ob1sdihBCCCGEsO8C8EAa9Q8kvucQZ3okLAI6AZ84cW5aGgElgO0qaapvd6CVUuppoAaggEBS\n9koIBHbYa3jq1KksWLAgRV1wcDDBwcEuCl0IIfKn1cdWM3fPXL584EuK+xa3OhwhhBBCCGFfCDBT\nhao2wJbEumbA/cBwRxtzJpFwBJikFM2BPUCKXgRa8z8H21sN1E1V9xWwH3hTa31MKXUWaA/sBlBK\nFcLc9If2Gh4zZgwDB8p65kJktUvRl1hycAmD6w+Wpf/ygetx1xm5dCStK7Tm0fqPWh2OEEIIIYTI\ngA7RX6lQtR94FuiTWL0fuE+H6C3pn5k2ZxIJTwDXgNaJJUV84FgiQWsdBexLXqeUigIuaK33J1ZN\nA15TSh3BLP84CQgDfnQ0eCGE620O28yQH4dQo3gNmpVtZnU4xMTA5s3QOvVPKOESoetCCbsSxs8D\nf5bEkRBCCCFELpGYMHDJJ+0OJxK0pqIrLpzRZVJeU7+llPIFZgCFgY1AF611bDbEIoTIQKfKnagQ\nUIEZ22bkiETChx/Cyy/Dtm1QN3V/J3FHtp/ZztQ/pjKp7SSqFatmdThCCCGEEMJBKlQVALyS1+kQ\nfcWRNu5o4TSlUErh8o+jtNbttNb/l6pugta6tNbaV2vdWWt9xNXXFUI4x93NneF3D+ebv7/h8o3L\nVofDk09C1aowZAjEyRSuLnMz4SbDlgyjdsnajL13rNXhCCGEEEKITFKhyleFqg9UqIoAooBLqYpD\nnEokKMVgpdgDRAPRSrFbKR5xpi0hRN4wtOFQ4hLi+HrX11aHQoECMGsW7NoFb7xhdTR5x3t/vMeu\n8F3M7DETT3dPq8MRQgghhMiVlFJPKaWOK6WilVKblVJNMnleC6VUnFJquxOXfRtoB4wCYoBhmAkY\nTwODHW3M4USCUvwf8DHwM/BQYlkOfKIUzzvanhAibyhVsBR9avbhg78+IEEnWB0OjRvDuHEwaRLs\n3Gl1NLnfkYtHeH3d6zzX7DmalMnU7zohhBBCCJGKUqo/MBXzEN8Q2AWsUErZXQZLKRUAzMIsVuCM\nHsCTOkR/C9wENuoQPRl4BSfmTXCmR8IzwCiteUlrliSWF4EnMTNACiHyqWeaPsOhC4dYdXSV1aEA\nMH481KoFjz4KsTKjitO01oxYOoIg/yAmtp1odThCCCGEELnZ88AMrfVsrfUBYCRwHRiawXmfAHOB\nzU5etyhwLHH/SuJrgN+AVo425kwioRTwexr1vye+J4TIp1qUa0GDoAZM/3O61aEA4OVlhjjs22d6\nJgjnfLXzK9YcX8OM7jPw8/KzOhwhhBBCiFxJKeUJNAJ+tdVprTWml8E9ds57DKgIhN7B5Y8ltgFw\nADOyAExPBYcnOXMmkXAk2UWT6w8cdqI9IUQeoZTiP+3+w+D6Dg+zyjINGpieCT//LBMvOuPstbOM\nWTmGwfUH06lyJ6vDEUIIIYTIzYoD7kB4qvpwICitE5RSVYEpwECt72j88JdA/cT9N4GnVKi6AbyH\nmT/BIQ4v/4gZy7FAKVoBmxLrWgDtSTvBIITIR7pW7Wp1CLcZN84sB+kp8wM6bPTy0Xi4efBup3et\nDkUIIYQQIl9RSrlhhjOEaK2P2qqdaUuH6PeS7a9WoaoGpnfEER2idzvansOJBK35VimaYcZ29Eqs\n3g801ZodjrYnhBBZTRIIzllycAkL9y5kXp95FPMtZnU4QgghhBA5xvz585k/f36KurCwsIxOOw/E\nA4Gp6gOBs2kcXxBoDDRQSn2YWOcGKKVULNBJa73OschBhaoCOkSfAE44eq6NMz0S0JptwCBnLyqE\nECJnuxJzhSeXPUnXql0ZUGeA1eEIIYQQQuQowcHBBAcHp6ibO3cugwal/5istY5TSm3D9OZfAiYj\nkPj6f2mccgWok6ruKaAt8CDwT2bjVaHKHbNCw0ggUIWqajpEH1OhahLwjw7Rn2e2LXBu+ceuStE5\njfrOStHF0faEEELkPONWj+Pyjct83O1jzO83IYQQQgjhAu8Cw5VSg5VSNTCrMfgCXwEopd5QSs0C\nMxGj1npf8gJEADe01vu11tEOXPdVYAjwIpB8PbO/gWGO3oQzky2+mU69svOeEEKIXGLN8TV8tPUj\n3mj/BuUDylsdjhBCCCFEnqG1XgiMBSYCO4B6QGet9bnEQ4KAcllw6cHAEzpEz8UMr7DZBdRwtDFn\nhjZUBQ6mUX8AqOJEe0IIIXKIKzFXGPrjUNrc1Yanmj5ldThCCCGEEHmO1voj4KN03nssg3NDcW4Z\nyDKYFRhTcwMcnlHMmR4JkUClNOqrAFFOtCeEyMPiE+K5FnvN6jBuk5AAEybAd99ZHUnOMnblWC5E\nX+CLnl/gppz5FSGEEEIIIXKgfUDLNOr7guOLJjjzV+KPwDSlqGyrUIoqwFQSJ4wQQggArTVNZzZl\n4vqJVodyG6Vg3z549FE4cMDqaHKG5UeW89n2z3in4ztULFLR6nCEEEIIIYTrTAQ+UKHqJUweoI8K\nVZ9h5k5w+I91ZxIJL2J6HhxQiuNKcRyz/OMFzFgPIYQAQClFmwptmLl9JtfjrlsdTgpKweefQ/ny\n0Ls3XL1qdUTWunzjMsOWDKNT5U480egJq8MRQgghhBAupEP0j0APoAPmeX4iUBPooUP0Kkfbc3iO\nBK2JVIp7gY5AfSAa2K01GxxtSwiR9z3V9Cne2/we8/bMY9jdDk8Im6UKFjRDG5o0gSFDYPFik2DI\nj0YvH83V2KvM7DFTVmkQQgghhMiDdIjeiHmOv2NODYDVGq01K7Xmba354E6SCEqpkUqpXUqpyMTy\nu1Lq/lTHTFRKnVZKXVdKrVJKyaSOQuQSlYpUolu1bkz/czpaa6vDuU316jB7tkkovPWW1dFYY8nB\nJczeNZtpnadRLiArJgkWQgghhBBWUqHqmApVxdKoL6xC1TFH28sJM2mdAl4C7gYaAWuAH5VSNQGU\nUi8BTwNPAE0x3TBWKKW8rAlXCOGoZ5s+y+7w3Ww8udHqUNLUqxe8+iq88gqsXm11NNnrwvULPPHT\nE3Sv1p0hDYZYHY4QQgghhMgadwHuadR7Y1Z0cIgzyz+6lNZ6Waqq15RSo4DmmLkXRgOTtNZLAZRS\ng4FwoBewMDtjFUI4p0OlDtQoXoPpf06nVYVWmTpHa7h4ESIiIDw8qVy4ANHREBOTsty4YbaxseDl\nBQUKgI/P7VsfHyhaFIKCkkpgIISGwtatZojD0aPg7Z21X5Oc4ulfniY2PpZPu38qQxqEEEIIIfIY\nFap6JnvZWYWqyGSv3YH2wD+Otmt5IiE5pZQb8BDgC/yulKoIBAG/2o7RWl9RSm0B7kESCULkCkop\nnm7yNKOXj+ZU5CnKBZRDazh3Dg4dSln++cckDCIi4ObNlO14e0Px4iYZ4O1tkgPe3imLnx/ExUFU\nVFLS4caNpO3163D5MsTHp2w7IABKlDBJhWHDoFo1M+yhenWoWhV8fbPty5VtFu9bzDd/f8PcPnMp\nVbCU1eEIIYQQQgjX+yFxq4FZqd6LwyQRxjjaaI5IJCil6gB/AAWAq0BvrfVBpdQ9mBsOT3VKOCbB\nIITIBS5cgDIXBlNMf8xjY45yZXc5Dh2CyMR8qFJm9YRq1czEh7ZeAiVLmq2tFCrkmskQ4+NNTGfP\npizh4WZ77BgsXw7nzyedU65cUnKhRg1o1AgaNMi9CYaIqAhGLRtFn5p9CK4TbHU4QgghhBAiC+gQ\n7QagQtVxoIkO0eczOCVTnEokKEVl4DGgMjBaayKUogtwUmv2OtHkAcwKEAFAX2C2Uipz/Z+FEDnK\npUuwfbsZJrB1K2zbBsePAxSkUMAebtRR1K0LDz5oPumvVg0qVza9DLKLu7tJUpQsCfXqpX/cxYum\nl8TBg0nbjRth5kwzhMLNDWrVgsaNk0q9etl7L87QWjNy6UgAPu72sQxpEEIIIYTIo1SougcopkN0\nxWR1g4FQwA/TY+EZHaJjHGnX4USCUrQGfgE2Aa2AV4EITCLgcUwiwCFa65uAbabIHUqpppi5Ed4C\nFBBIyl4JgcCOjNqdOnUqCxYsSFEXHBxMcLB8+iaEq5w+DevWmbJ+vXngBvD3N5/a9+ljto0bQ+XK\nCrecMMVrJhUtCs2bm5JcbCzs3ZuUKNm6FebONUMq3N2hTh1o2RLatIFWrcyQiZzkix1f8P2B71nU\nbxEl/UpaHY4QQgghhMg6IcBawMw5GKrqAp8DX2HmJHwBOA1McKRRZ3okvAm8pjXvKsXVZPVrMKsr\nuIIb4K21Pq6UOouZAGI3gFKqENAM+DCjRsaMGcPAgQNdFJIQAkziYP16WLvWJA8OHzb1tWpBhw4w\nfrxJGlSrRq5KGjjCywsaNjRl+HBTFxMDe/aYxMKWLfDLL/DBB+a9WrWgdWuTWGjd2gzTsMr+c/t5\ndvmzPN7wcfrWcjjvK4QQQgghcpf6wGvJXg8AtugQPRxAhapTmN4JExxp1JlEQl3g4TTqI4Dijjam\nlJqC6eFwEigIDARaA50SD5mGWcnhCGYiiElAGPCjo9cSQjguJgY2bIBly8zDsa3HQa1a0LEj/Oc/\n5uG4ZB7/YPvyZShcOP33vb2ThjeMGGHqwsJM0mX9erOs5Mcfm/rq1c3XrmtXk1zIrqEQN27eYMC3\nAygfUJ73738/ey4qhBBCCCGsVISUvfttIwxs/gLKOdqoM4mEy0Ap4Hiq+obAv060VxIze2QpIBLT\n86CT1noNgNb6LaWULzADKAxsBLporWOduJYQIhP+/Rd+/tkkD1avNisglC1rHnwnTzbd9a38VD27\nXbhg5j544QV47rnMn1e2LAwcaAqY3hwbNpjeHD/9ZHosFChgkgldupivb5UqWXILALy46kUOnj/I\nlmFb8PPyy7oLCSGEEEKInCIcqAicUqHKC7gbM9zBpiBm9QaHOJNI+Ab4r1L0w6yo4KYULYB3gNmO\nNqa1HpaJYybgYFcLIUTmaW265P/wg0ke7NxphiXccw+8+ip06wZ167pmxYTcqFgxGDQInn8eihSB\nRx91rp3SpWHAAFO0hv37TS+PX36BsWNh9GiTSOjSBbp3NwkGLy/X3MOSg0uY/ud0pneZTv2g+q5p\nVAghhBBC5HQ/A2+qUPUS0Au4jvlw3qYecNTRRp1JJLyCmZ/gFOAO7EvczgMmO9GeEMICCQlmLP+3\n38LixXDihJlc8P77zSfvnTubB+isprXOFasGvPmmWcXh8cfNEIcHHriz9pQyw0Nq1YIxY+DaNViz\nxiQVfvwRpk+HgADo0QN69zbfDz8nOxH8e+VfHvvxMXpW78lTTZ66s8CFEEIIIURuMh74DlgPXAMe\n1SEpevcPBVY62qjDiQStiQWGK8UkoA7gD+zQmsOOtiWEyF7x8bBpk0kefPutGcIQGGgeVPv2NXMd\neDi1KKzjomKj6DK3C880fYZ+tftlz0XvgFLwySdmroSHHjKrNPR14VyF/v7Qs6cpWsOuXfD996bM\nmWPmUejUyayC0b27SfpkRnxCPAO/G4iPhw9f9PwiVyRthBBCCCGEa+gQfR5opUJVAHBNh+j4VIf0\nwyQYHOL0I4PWnMRMkCiEyMG0hs2bzYPv4sUQHg5lypgH0r59oUULs2RhdvPz8sPL3Yspv02hb62+\nueIB193dfB0ffdQkEz76CEaOdP11lIIGDUwJDYUjR5KSCo8+auJo2xb69zffR3tJhTd+e4MNJzaw\n9tG1FPPNhi4mQgghhBAix9EhOjKd+ovOtOdwIkEpFNAXaIuZKDHFAm9a08eZQIQQrrVvH8ybZ8rx\n42Z8/sMPQ79+0KxZzlia8dWWr9JudjuWH1lOl6pdrA4nU7y8TDKhRAkYNcoM/+iXxR0qqlQxw01e\neMFM2PjjjyYpNGKEiaFjRzPvwgMPmOEQNptObmLCugm81uo1Wt/VOmuDFEIIIYQQ+YYzPRKmASOA\ntZgZILVLIxJCOO3UKfjmG/Ogu2uXGcvft69ZNaBlS2t6HtjT5q42NC/bnCm/Tck1iQQwSZj334cm\nTcwcBtmpdGmTPBg1Cs6eNQmFBQtMTwUvLzNR44AB0KLDJR7+7mGal23O661fz94ghRBCCCFEnuZM\nIuERoI/W/OzqYIQQjrt61cx3MGsWrF8P3t7m4TY01Eyc6O1tdYTpU0rxyn2v0PObnmw8sZGWFVpa\nHVKmKQWPPGJtDEFB8PTTppw6BYsWmaRCcLDGLXg47lWuMKH8XHS8R6q+Y0IIIYQQQjjPmT8tI4Fj\nrg5ECJF5CQnw668weLB5mBw61PQ2+PJLMwfCwoWmm3tOTiLYdKvWjbol6/Kfjf+xOpRcrVw5+L//\nMytxhCz7gITq3xK4eSZDH6xA6dIm2bB5s5kzQwghhBBCiDvhTCJhAhCiFD4ujkUIkYFDh+C11+Cu\nu6BDB/Ng+Mor8M8/sHq16d5eqJDVUTrGTbkx7r5xrDi6gm2nt1kdTq637p91TN76PM83f56TKx5k\nxw4YMsRM1HjPPVC1KoSEmP+XhBBCCCGEcIYziYSFQBEgQin2KMX25MXF8QmR7125AjNnwr33QvXq\n8MEH0LUr/P47HDwIr74K5ctbHeWd6Ve7H3VL1mVPxB6rQ8nVTkaepN+ifrS+qzVvdXzr1uoPb78N\nJ0+aXiytW8O0aeb/pebNzcoTFy5YHbkQQgghhMhNnJkjYRbQCJiDTLYoRJbQGn77DT7/3Ix7j46G\nTp3MRIo9e4JPHusP5OHmwY4RO3B3y2GzQd6BiROhbl3o3Tt7rnc97jq9vumFv5c/C/ouwMMt5Y93\nd3do186UDz6ApUvh66/h2Wfhueege3czVKZrVzNpoxBCCCGEEOlxJpHQDeisNb+5Ohgh8rt//4XZ\ns+GLL+DIEahUCcaNM0MWypWzOrqslZeSCPHxsGePGULwwgswZQp4OPPTNpO01jzx0xMcOH+APx7/\ng+K+xe0e7+Njlqzs1w8iIkyCavZsk/QoVsys+jB4sFmVQqmsi1sIIYQQQuROzgxtOAVccXUgQuRX\ncXFm/Hr37maIwqRJZiz72rVw+LCZEyGvJxHyGnd3M+HlO+/Au+9C+/Zmqcas8t7m95i7Zy5fPPAF\n9YPqO3RuyZKmV8LWrSb58fjj5v/HZs2gVi14800IC8uiwIUQQgghRK7kTCJhDPCWUtzl4liEyFcO\nHoQXX4SyZaFPH/PJ8EcfwZkz5tPhNm3ATZbsy7WUgjFjTELo0CFo2BA2bnT9dVYfW80Lq17gxXtf\nZECdAXfUVp068N//mvkUVqyARo3MEI3y5c3Qmrlz4fp1FwUuhBBCCCFyLWceU+YAbYGjSnFVKS4m\nLy6OT4g8JSoKZs2Cli2hRg0zB0JwMOzeDX/+CSNGQECA1VEKV2rZEnbsgGrVoG1b00PBVUswHr90\nnP6L+9OhUgemtJ/imkYxPSo6dYI5c0xPipkz4cYNGDTILDc6bJhJishSkkIIIYQQ+ZMzo3afc3kU\nQuRhWsO2bSZpMG+eWYWhQweYPx969YICBayOUGS1oCCzYsKrr8L48WYugooV76zNqNgoei3oRZEC\nRZj/4Pwsm2OiUCEYOtSUY8dMb5nZs83/z5UqmbkUBg++8/sRQgghhBC5h8M9ErRmlr3iaHtKqXFK\nqT+VUleUUuFKqe+VUtXSOG6iUuq0Uuq6UmqVUqqKo9cSIjtdvAjTp5vl95o0gZ9+gtGjzcPYqlVm\nQjtJIth3Neaq1SG4jIeHGTZw+PCdP3RrrRm6ZChHLx7lhwE/UNSnqGuCzEClSjBhgpkIdP16M/xm\n6lRT37q1mST0isygI4QQQgiR52UqkaAUhZLv2ytOxNASmA40AzoAnsBKpdStBe6UUi8BTwNPAE2B\nKGCFUkoWKRM5SkKC+eQ5OBhKl4b/+z+oUgWWLYMTJ8x4c/nkNnNm7ZxF1elVibwRaXUoLlW69J23\nMXnDZBbuXcisXrOoU7LOnTfoIDc3aNXK9Eo4e9YMgfD2NkMegoLMEIhVq8zqFUIIIYQQIu/JbI+E\nS0pRMnH/MnApjWKrd4jWuqvW+mut9X6t9R5gCFAeaJTssNHAJK31Uq3138BgoDTQy9HrCZEVwsJg\n8mSTNOjQAXbuNK/DwuDbb6FrVzPuXGRex8oduRp7lSkbXTf2Py+YuX0mr697ndA2oTxY60Grw8HX\nFwYOhJUrTbJs/HizAkSnTlChArz8Muzda3WUQgghhBDClTKbSGgHtyZSbJv4OnWx1d+pwoC2XU8p\nVREIAn61HaC1vgJsAe5xwfWEcEpMDCxaBF26mAemN94wXb1/+w327YOxYyEw0Oooc6/SBUvzUouX\nmLZlGscuHbM6nGxjb1WEJQeXMGLpCEY1HsX4VuOzL6hMKlcOxo2D/fthyxZ44AH47DOzGkSjRvD+\n+2Z1EiGEEEIIkbtlKpGgNeuBV5TCV2vW2yt3EoxSSgHTgN+01vsSq4MwiYXwVIeHJ74nRLbavRue\new7KlIGHHoLISJgxwyzb+MUX0KKFWfpP3Lkx94yhhG8JXl79stWhZIvffzdJqTlzbl8RYdPJTfRf\n3J/eNXozvct0VA7+n0wpaNoUPvzQ/Lv4/ntzXy+8YIZ2dO8OCxdCdLTVkQohhBBCCGc4MtliCOCf\nVYEk+gioBdzZYuhCuNjly/Dxx9C4MdSvb1ZceOwx02X799/N2PBCzswQIuzy8/JjSvspLNq3iE0n\nN1kdTparWhU6doRHHjFDA3bvNvV7I/bSfX53mpdtzpw+c7JshYas4OVlVif57juTVJg+HS5cgP79\nzXwKjz8Oa9ea+UWEEEIIIfIDpdRTSqnjSqlopdRmpVQTO8f2VkqtVEpFKKUilVK/K6U6ZWe8acal\nM7kQuFIkAEFakyUdU5VSHwA9gJZa65PJ6isCR4EGWuvdyerXATu01s+n0dbDwNyGDRtStmzZFO8F\nBwcTHBycFbcg8pj4eDNh3FdfwQ8/wM2b0K2bWQava1fw9LQ6wvwhQSfQ5LMmuCt3Ng/bjJtyeLGZ\nXGfpUjNR55Ej0G/YSTZUu5dA/+KsH7KegAIBVofnEocOwddfw9y5cPy46eHz8MNmosZ69ayOTggh\nhBDCcXPnzmXQoEEAA7XW89I6RinVH5iFWUjgT+B5oB9QTWt9Po3j3wP+BdZi5iUcCowFmmqtd2XF\nfWSGo4mEQK055/IgTBLhAaC11vq2wdBKqdPA21rr9xJfF8IMbRistV6UxvEPA3PnzJnDwIEDXR2u\nyOP274dZs8xDzunTUKuW6X0wcCCUKmV1dPnThhMbaP1Va+b0nsPAevnj33RcHLz78QVePXofCW43\neK7Q70x8oRT+Wd0vLJtpDZs3m+EcCxaY3gp16piEQnAwlC9vdYRCCCGEEJmTyUTCZmCL1np04msF\nnAL+p7V+KzPXUUr9DXyjtZ7smsgd5+hHe4eU4qK94mgASqmPgIHAw0CUUiowsRRIdtg04DWlVA+l\nVF1gNhAG/Ojo9YRIy6VLZuhC8+YmcfDpp9C7N/z1F/z9t5k4UZII1mlVoRVvtH+Du0vdbXUo2SZW\nR/GDX3eKlLnAsAIr+XxaKc65PI1rPaXgnnuS5lP46SeTSJgwwcyr0KoVfPQRefLehRBCCJG/KKU8\nMasTJl9IQAOryeRCAomJh4Lg+LO3K3k4eHwI4OpF3UdiJlNcl6r+MUzCAK31W0opX2AGZlWH+Ooh\nMAAAIABJREFUjUAXrXWsi2MR+UhsLPzyi+l58NNPZihDly6weLGZDM7b2+oIRXIv35c/JlwEiIuP\no//i/uwJ38O6IetoXLoqU1+BggWtjixreXqaf3vdu8PVq2ZehW++gWefNaVDB9NLoVcvCMgbIzyE\nEEIIkb8UB9xJeyGB6pls4wXAD1jowrgc5mgi4RtXz5Ggtc7kyhF6AjDBldcW+Y+tG/XXX5tu1Bcv\nQoMGMGWKGboQJOuACIvFJ8QzdMlQVhxdwbKHl9G4dGMg7ycRUitYEB591JRz50yCb/58GDLEJPm6\ndjVJhW7dwNfX6miFEEIIIbJe4hD+8UDPtOZTyE6OJBIyN5mCEDnQ0aNmDPacOWYCuzJlzEoLjzxi\nulELkRPExcfxyPePsHjfYuY9OI9OlTM/Ie/Fi+aBukCBjI/NbUqUgFGjTDl1yiwdOX++WX7Vz8/0\nYHjoIdOjyMfH6miFEEIIkR/Mnz+f+fPnp6gLCwvL6LTzQDwQmKo+EDhr70Sl1ADgU6Cv1nqtQ8Fm\ngRyzaoMryWSLAsx4a9sDx5Yt4O8Pffua5EHr1uCee1bQE/lAzM0Y+i/uz8+Hf2ZB3wX0rtnbofNH\njTKrizz/PIwcmT+WIz182PwbX7QIdu0ySYUePaBfP0kqCCGEECL73cFkiycxky2+nc45wcBMoL/W\nemmWBO+gTE+2qDVuuSGJIPK3S5dg5kxo3x7KloUXXoDAQJg3D8LD4csvoV07SSKInCU6LppeC3qx\n/Mhyfhjwg8NJBDDLRXbvDq+9ZlY6GDcOTpzIgmBzkKpV4dVXYedOOHjQ3PP+/fDgg1CypBn68N13\nEBVldaRCCCGEELe8CwxXSg1WStUAPgF8ga8AlFJvKKVm2Q5O/JB8FjAG+CvZ4gSWfmyU9xdkF3le\nVJTpddCzp0kaPPGEqZ8xwyQPfvzRPFDIOOq8KbO9qnKqa7HX6DavGxtObGDZw8voWrWrU+1UrQqf\nfQbHj8Pw4WYVhIoVzVwCP/4IN2+6OPAcplq1lEmFl1+GfftMUqFECejTx8yNcumS1ZEKIYQQIj/T\nWi8ExgITgR1APaCz1tq2RlUQUC7ZKcMxEzR+CJxOVqZlV8xpkUSCyJWuXTOTJfbtax4SHn4YIiLg\n7bfh33/h11/NHAhFilgdqchKL656kVd+fcXqMJwWeSOSznM6s/X0VlYMWkH7Su3vuM0yZcy/g9On\nTWLhwgWzysHevS4IOJewJRV27TLDH0JDzVCnwYNNT4XOnU2i8azdkYhCCCGEEFlDa/2R1vourbWP\n1voerfXWZO89prVul+x1W621explqDXRG5JIELnGtWtmKTjbJ4wDBpiu2xMmmMkUN2+G0aOhVCmr\nIxXZpZhPMf676b9sPLHR6lAcdjH6Ih2+7sC+c/tYPXg195W/z6Xt+/vD44+b+UH27YP69V3afK5R\npYoZ4vTHHxAWBtOmmaVen3oKSpeG++6Dt96CAwfMqi5CCCGEECJjkkgQOVpkpJnfoE8fkzwIDjaz\ntk+cCMeOwV9/wYsvQqVKVkcqrDD23rG0KN+CwT8M5krMFavDybSIqAjazmrLP5f/Ye2ja2lapmmW\nXq9mzYyPSUjI0hByhDJlTAJh9Woz7OmLL6BYMZOMrFkTqleHsWNhw4a8PxRECCGEEOJOSCJB5Dhn\nzsAnn5juxyVKwMCBZrjCpElm/Peff5pPGCtWtDpSYTV3N3dm95rN+evneW75c1aHkylHLh6h1Zet\niIiKYN2j62gQ1MDqkLhwwUxOOmIErFljPrHP64oVgyFDzPwRFy7ATz9B27Ymcdm6tZlv5ZFHzIoQ\nkZFWRyuEEEIIkbNIIkHkCIcOme7F99xjuhs//bT5RPDdd+HkSdM9e+xYuOsuqyMVOU3FIhX53/3/\n48udX/L9/u+tDseutcfX0mxmMxJ0AhuGbKB2ydpWhwSYxMEjj8CKFWbFkzJlzL/BX3+F2Firo8t6\nPj5mxYsZM8zwhz//hCefhD174KGHoHhxaNPG/Izas0eGQAghhBBCSCJBWOLmTfjtNzOzeu3apkvx\nhAkQFASzZpmJE3/91TzMlCuXYXMinxvSYAi9avTiiaVPcPZazpxB75Otn9BpTifuLnU3W4ZtoWqx\nqlaHdEvJkvDf/5oeP5s3m8lLf/gBOnQwn9w/9FD+eXh2c4MmTUwPqJ07zTwsH3wAAQFm0sZ69czy\nmiNGmK/R1atWRyyEEEIIkf0kkSCyzaVLZrLEQYNMt+GWLc0Y5SZN4Pvv4fx5sx08GIoWtTpakZso\npfi0+6e4KTee/vlpq8NJIS4+jqd/fppRy0YxqvEofhn4C0V8cuZyIkpBs2amJ9CpU7B9O4wbZyYw\nVcrq6KxhSxrYhkCsXGlWi1m3Dnr3NomWdu3gjTdg27b8MdeEEEIIIYSH1QGIvEtrM1v88uWwdCls\n3Gi6UDdoYLoNd+9ukghuks4SLlDCrwQL+y4kyD/I6lBuuRh9kYcWPcT6E+v5pNsnjGg8wuqQMk0p\naNjQlIxobYYf3X03eHllfWxWKVAAOnY05b334MgR+OUXk1z4z3/glVdMYqFDB+jUyRwnPaqEEEII\nkRdJIkG41OXLZkb0FStMAiEszPzx3b696R7crZv8YS2yTuu7Wlsdwi0Hzh+gx/weXIy+yKpHVtHm\nrjZWh5RlDh0y85v4+ppt69amNG1q/v3nVVWqwDPPmBIba4aFrFxpyrBhJsFSo4ZJKLRrZ74mRXJm\nZxQhhBBCCIdIIkHckfh40/3ZljjYvNnU1ahhuv/efz+0amUmMxMiv1h+ZDkDFg+gTKEy/DnsTyoX\nrWx1SFmqcmXTI2H9elPeeQdef930Tqhf3yQU3nwT/P2tjjTreHmZn3WtWsHkyWYYxJo1JqmwbBlM\nn57Uy6NdO7NCRMuWULCg1ZELIYQQQjhOEgnCIVrD4cOm18Gvv8LatWbug0KFTHfejz4yyzZWqGB1\npEJkv7j4OCaun8iU36Zwf5X7mf/gfAp5F7I6rCzn4WGSBU2bmqVZ4+Nh924zoepff5kEo6+v1VFm\nr2LFoF8/UwD++cf8vFy7FubPN8kWd3czvKttW5OAuPde87NUCCGEECKnyxGJBKVUS+AFoBFQCuil\ntV6S6piJwDCgMLAJGKW1PpLdseZHp0+bpIGthIWZB4dmzUyX3g4doHlz8PS0OlIhrHPw/EEGfT+I\nHWd2MKH1BF5p+Qrubu5Wh2UJd/fMz69gM3KkWc2lXj1T6tY1D+N5xV13wWOPmWJLyK5da3otfP65\nmazRzc18zVq2NImF++6DEiWsjlwIIYQQ4nY5IpEA+AE7gc+B71K/qZR6CXgaGAz8A0wGViilamqt\n88Eq59krLMx0T96wwWwPHjT19eubZeDat5cuuULYaK35ZOsnjFk5hnIB5fjj8T9oUqaJ1WHlOp6e\n8Oef8PXXZr4BgNKloWZNszzsI4+YhGVeoBRUq2bKiBFJiYUNG0z5/nuYNs0cW7Om+XnbooXpsVC5\ncv5dQUMIIYQQOUeOSCRorZcDywGUSvNPpNHAJK310sRjBgPhQC9gYXbFmRdpbbrcJk8cHDtm3qtV\ny3S5DQ0125IlLQ1VCKdtOLGBtza9xaJ+i/DxdN2EHeHXwnl8yeMsO7yMkY1G8k6nd/Dz8nNZ+/nJ\n9Olme/OmeajevduUAwfMz6Y2bewnEi5dMvMSlC+f+1aOSJ5YGDbM1J08aVa62bDBbD/91NSXKGES\nCrbSqJHMQSOEEEKI7JcjEgn2KKUqAkHAr7Y6rfUVpdQW4B4kkeCQmzdh1y74/XfYtMmUsDDzh2y9\nemZVhdatzSdgkjgQeYWXuxdrjq9h4HcDWdRvkUuGHCw5uIRhS4ahlOKn4J/oXq27CyIVHh7mU/ia\nNaF//8yft2QJDBlifpaVLWuGElSsaLblypkEQ6dOWRR0FihfHgYONAXg4kUzoeXvv5sycSJERZme\nHA0bmiSLbZ6KKlWk14IQQgghslaOTyRgkgga0wMhufDE94Qdly/DH38kJQ62bIHr180ndo0bw4AB\nJmlw331QtKjV0QqRNZqXbc6CvgvotaAXo5ePZnqX6aTd+SljV2OuMnblWD7d/ik9qvVgZs+ZlPST\nrJvVunc3c7gcOwbHj5ty6BCsWgVnz0JgIJw5Y7+NdevMA3hQkCmFCuWcB/KiRaFLF1PAJIX//jsp\nsfDLL/C//5n3ChdOSio0bWomdAyS35ZCCCGEcKHckEgQmRQba7oCb9lixhr/+afpFgymO2yLFjBh\ngtnefXfeXt9diNR6VO/Bx90+ZsTSEQR4BzC53WSHkglaa+btmccLq14gMiaSGd1nMPzu4U4nJIRr\nFStmllVs1+729+LizLCHjDzzjHk4t/HxMQmIEiVMGTzYsV4SWcnDAxo0MOXJJ03dxYuwdWvSz/9P\nPzVLUYLppXH33WYohG1bqpR18QshhBAid8sNiYSzgAICSdkrIRDYYe/EqVOnsmDBghR1wcHBBAcH\nuzpGS4SHm0/gbImDHTsgJsZ0da1f30yK+PLLZhytdHUVAp5o9ARXYq7wwqoXOH75OF888AUFPDLO\nqO04s4NnfnmGTac20bdWX97p+A4VCssap7mFp2fmPpH/7TfTeyF5OXMGzp+Hc+fMspb27NqV1Lur\nSJGkbeHCEBBgyjPPZF3vr6JFzfAN2xAOreHUKfM7Yts22L4d3n/fJBzAfE1siYW77za/N+66S35X\nCCGEECJjOT6RoLU+rpQ6C7QHdgMopQoBzYAP7Z07ZswYBtoGmOZBf/xhxs9WrWq6rwYHmyUZ69eX\n3gZCpGfsvWOpEFCBwT8M5kTkCVYOWpnuBIkXrl/gtTWvMWPbDGqWqMnqR1bTvlL7bI5YZBfbw371\n6s6dHxhoen1dumTKxYum/PsvREaaMny4/TZeeAE++cSsilOwIPj7m62fnyl168L48fbb2LTJJAN8\nfMDX1/xeaNMGvL1NOXPGJBW2bzcJhk8+MYkSMMM56tUzv0dspU4d044QQgghhE2OSCQopfyAKpie\nBwCVlFL1gYta61PANOA1pdQRzPKPk4Aw4EcLws0xOnUy3XVlbgMhHNOvdj/KBZTju/3f4et5+xNS\nfEI8M7bN4LU1r5GgE3iv83s82eRJPN09LYhW5BZBQTBmzJ210aOHaefaNbh6Nalcv26SE+GpZwtK\nQ+/eSYmBtPzvf6ZnRJ8+5rXWJrmwa5cp69bBl1+aa9oUKmR+1xQvbibinTjRJBk80vkrYssWOHLE\n9AaxFQ+PpG3RoiYpYk9EhEmIeHiY4u6eVNzcTBFCCCGENXJEIgFoDKzFTKqogamJ9bOAoVrrt5RS\nvsAMoDCwEeiitY61IticwtdXPiUSwlnNyzanedmU6wlqrVlzfA1jVo5hV/guhjYYyhsd3pDJFEW2\nadXKlDvx119mRYfr1yE62pQbN5JKkyYpj1cKSpc2pUsXk8yYNMmcb+tVcfmy2YaFmYkef/7ZTNpb\no4bpsVCnTtJqG5Urm0TEjBnpx9i2LaxZY/8+atZMGoaRlg8/TJofIi2bNsFDDyUlHpInIGz7Gzea\n4SfpmTrVrAhiO89WlDLbBg1gyhT79/HEEyYxZDvPdq5tf/Bg02MkPQcPwgcfJB1vG3qS/PXEiabH\nSnp+/NEMgUx+bvI2KlY0q57Y8+675v+l1Ofbtp07mxVE0nPiBCxalPQ6+RAa2/6oUfaXM12zBvbs\nuf18m7JlkxJk6Zk50wwDTa+N1q2hdu30zw8Lg6VL7V9jyBD7PUM3boT9+9N/v3RpM4GsPbNnm/tI\nbyhSixbm31B6Tp82k7TaM3Cg/fvYtClpLq60lCoFXbvav8bcuUnfj7Tce6/5OZOe06dh+XL713j4\nYfv38fvv5t9ZekqVgvvvt3+NefPs38c992R8HytW2L9GcHDuv4++fU0vO5E35IhEgtZ6PWD3swWt\n9QRgQnbEI4TIX7TWLD+ynEkbJvFH2B80K9OMLcO20LRMU6tDE8JhFe5w+o7ateGbb9J/PyIC9u41\nE1PatsuWmaEbYHodVK4MPXuaoXdVqiQtxentbSa/9PbOOI5580ziIz7eJC9u3jT7tnLvvfbPL13a\nPMQnJCSdk5CQsnh52W+jaFGzfGhCgum5ER9vtlqbuvR6ZKT+ekVGJp1jO99WLl2yf/7ly7BhQ9Lx\ncHsbGQ13+esv8/VMfm7y/RYtMk4kfPaZSezYzrWdbxMUZD+R8M8/SZN/Jj8vuSFD7CcSfvgBPv88\n/fPbtMk4kTB+vP0E1fTp9hMJhw7B00/bv0a/fvYf+ObNyzjRllEi4fnn7d/Hxx/bTyQcOADDhtm/\nRq9e9u/j668zvo+MEgnPPpvxfdh7cD1wAB5/3P41HnjA/n3Mnp3xfWT0AP7MM3d+H0OH2r9Gz565\n/z7atpVEQl6idHo/jXMxpdTDwNw5c+bk6TkShBB3RmvNkoNLmLxxMltPb6V52eaMbzWeLlW6yGoM\nQjhAazM55f79KcuBA+YTKpvSpaFatdtLxYoZP9QLIXKWzDxCZPSr9E7bSJ4Ys3e+vTYSEjKOIaOh\nVBm1kVEMGd2H1kk9mezFkNHXwt3d/vsZTSps61GVHlvCND0Z3UNeMHfuXAYNGgQwUGs9z+p4slKO\n6JEghBDZKUEn8O2+b5m8cTK7w3fTukJrVj+ymnYV20kCQQgnKGW6zZYqdfsSnJGRJqFw6FBS+fNP\nmDMnaR4Gd3fTY6Fy5ZSlShWoVEmG8QmRE7ni1+WdtpHRA3pmuGK+lTttI6fcR0aJhowodedtiNxD\nEglCiHzjxs0bLNy7kP9u+i/7zu2jQ6UOrB+ynlYVUg5K11rz4MIH6Vy5M8MbDcdNyaxuQjgrIMCs\nHNGsWcp6rU1vBVty4cgRU377DWbNSjnZY6lSJrFgGyJRsWJSKVMmc0MMhBBCCOE68qtXCJHnHTh/\ngE+3fcqsXbO4GH2RrlW78nnPz2+bbNHmxs0bFClQhJHLRjJ3z1w+7fEpNYrbGRQohHCYUiYJUKaM\nGTebnNZmhYojR+Do0ZRl9WqzyoSNh4eZx6BiRShf3swRUb580n65crIkshBCCOFqkkgQQuRJMTdj\n+G7/d8zYNoP1J9ZT3Lc4jzd8nOF3D6dqsap2z/Xx9OHzBz5nUL1BjFg6gvqf1OfVlq/y8n0v4+Uu\nA7mFyGpKmcn7goLgvvtuf//GDbMKwPHjpvzzj9nu329mDE+eaACzZGWFCmZW/7JlTXLBtl+2rJm7\nITMTQAohhBDCkESCECJPOXThEJ9t+4yvdn3F+evnaV2hNfP6zKNPzT54ezj2pNC2Ylt2jdzF5A2T\nmbRhEgv2LuDT7p/SonyLLIpeCJEZBQpA9eqmpCUmxizTd/JkUjlxwtStWQOnTsGVKynPKVnS9I4o\nXTppm7yUKQPFi7tmHLIQQgiR20kiQQiR6x2+cJhF+xaxeN9idpzdQVGfojxa/1GeaPTEHQ9J8PH0\n4T/t/8OAOgMY/tNw7vvyPuY/OJ8BdQa4KHohhKt5eydN2JieK1fg339NciEszCQXTp82ZetWsw0P\nTzkLurs7BAaaORtsPSZspVQp815goElKFCqU92cnF0IIkX9JIkEIkSsdOH+ARXsXsXj/YnaH78bP\n04/u1boz7r5x9KjegwIerh0UXTewLpuGbmLm9pl0qdLFpW0LIbJfoUKm1KyZ/jE3b5pkgi3BcPq0\nWebSVvbsgZUrzX5cXMpzvb1NQsGWWLDtlyhhejaUKJGy+PpK4kEIIUTuIYkEIUSuEJ8Qz9bTW/nl\nyC8s3reYvef24u/lT49qPZjQegKdq3TG1zNr14hzd3NnROMRWXoNIUTO4eGRNCGkPVrDpUsmoRAR\nYUp4eMrtwYOwYQOcOwdXr97eRoECSUmGYsVMSb5ve120aFIJCJChFkIIIawhiQQhRI6ktebopaOs\nOrqK1cdXs+b4Gi7fuEwh70L0rN6TKe2n0KlyJ5f3PHAFrTVKPloUIt9QKunhvlatjI+PiYHz501S\nwVZsr8+fhwsXTPJh/36zf/68OSet6xYpYort+rbXhQunvy1c2CQhZNlMIYQQzpJfIUKIHOPM1TNs\nPLmRVUdXserYKk5EnsDDzYPmZZvzfPPn6VCpA03LNMXDLef+6Np1dhfB3wYzuP5g+tXqR+WidgZp\nCyHyJW/vzPV0sNEarl83CYVLl+DixaSS+vXZs3DgAFy+bN6LjEy/XT8/k1AICEhKLtiKbehH8mKr\nL1gwqfj7m7kjhBBC5C85969xIUSeFh0XzY6zO9gctpnNYZvZ8u8WTkaeBKBWiVo8UP0BOlbuSOsK\nrSnoXdDiaDPP092TeoH1mLRhEuN+HUejUo14qPZD9KvVj4pFKlodnhAiF1LKPPT7+ZllLB0RH28m\nlrx0KWVywVYuX065f+4cHD1qzrGV69ftX8PXN2VywZZgsJXUr/39k+7Hz+/2135+4OUlc0YIIURO\nJokEIUSWi46LZt+5feyJ2MPW01vZ8u8Wdp7dyc2Em/h4+NC4dGMeqvUQzcs2555y91C6YGmrQ3Za\nrRK1+KbvN0TFRvHz4Z9ZuG8hE9ZN4KXVL9GkdBOG3z2c4Y2GWx2mECKfcHdPGu7grJs3k5IKkZFm\njof0ypUrZhsVZYZlnDgB166lLGkN00grbj8/k6Tw9U3aT17n6ws+PunvFyhgtumVAgVM8fCQpIUQ\nQjhKEglCCJeJT4jn6KWj7Anfw56IPfwd8Td7IvZw5OIREnQCANWKVaN52eYMbTCUZmWbUbdkXTzd\nPS2O3PX8vPzoV7sf/Wr341rsNZYdWsbCfQvZf36/1aEJIYRDPDyS5mBwhbg4k2iwlWvXbn99/bop\nUVG370dFmWEcYWEQHZ30vm0/Ojrlsp0ZcXNLSjrYkgu24u19+37ybWaKl9ft+8m3qYskNoQQuYEk\nEoQQDolPiOdk5EmOXDySVC4d4fCFwxy7dIyYePNRUwnfEtQNrEuXKl2oU7IOdUvWpXbJ2vh7+Vt8\nB9nP38uf/nX6079O/wyPvRZ7jTNXz1ClaBWZsFEIkSd5eiZN+pgVtDa9HqKjTblxI2k/eblxI2VJ\nXhcdbdq4cSNpe+OGGf6RvD51sdXfKS8v83WyJRds+6nrbMXeaw+PlO+l9dpWl3ybej95SV3v7p7+\na9u+bSsrjQiRN+SqRIJS6ilgLBAE7AKe0Vr/ZW1UIrvMnz+f4OBgq8PI867GXOXUlVOcijyVcnvl\nFCcjT3L80nHiEsyC6R5uHlQsXJEqRavQsVJHKhetTK0Stahbsi6B/oF2ryPfz7StPraa3gt6U6RA\nEeqUrHNbKerjoo8EXUy+n3mLfD/znvz0PVUqqRfBnQzpcJbWptdFbKxJKqTeJt+3HZf8mOSv4+KS\njkl+7P798ylfPvjW+7YSFcVtdTdvpv/atm/bZpfkSYbkiYb0XmemuLnZr7Pt26tzc0u7Lvl7qY9x\ntiiVtP/bb/Np0yb4tvrk+/bes+1nZnun+2mVjN6Xz0Vu5+hzrVKqDTAVqA2cBP6jtZ6VDaGmK9ck\nEpRS/TFfvCeAP4HngRVKqWpa6/OWBieyRX76I8iVtNZci73GxeiLnLt+jrPXzhJ+LZzwqHDCr4Vz\nNirp9ZmrZ4iMSZriW6EoVbAU5QqVo1xAOeqWrEuVolVulfIB5Z1eQUG+n2lrX7E9Pz/8MzvO7uDv\niL/57eRvzNw+81bypmLhihx99miO660g38+8Rb6feY98T7OPUkm9BvyzqBNez57z+eIL134/tYaE\nhNuTDfHx5nVaJa33k7+2vW+rS72fVl3q/eQlvfqEhNvrkl/b9n7y45Lva51Ul/zY1Pvp1dnaSP7a\nMfOZOjVv//tUCurVg507rY7Eeo4+1yql7gKWAh8BDwMdgJlKqdNa61XZFXdquSaRgPkCz9BazwZQ\nSo0EugFDgbesDEyIrKS1JiY+hmux14i8EUlkTGS620vRl7gQfYGL0Re5EH2BC9fNvu0hNLliPsUI\n9A8kyD+I0gVL0zCoIUH+QZQLKHcrcVCmYJk8OX9BTlbQuyBdqnahS9Uut+ri4uM4fPEwf0f8TURU\nRIZJhLErx3It9hqBfoGU9CuZogT6B1K4QGHclPQtFUIIkUSppE/qxZ1LnVjQOmVd8jJwIMyefXu9\n7fi0zrftp65LXW/v/bSOTb2fXl1Gr9MqVvQQyqEcfa4dBRzTWr+Y+PqgUuq+xHYkkWCPUsoTaARM\nsdVprbVSajVwj2WBiXxHa01cQhyx8bEpSszNGGLiY4i5GcONmzeIiU/cJnsdHRdN9M1orsddJzou\ncXsz5TYqNoprsdduK/E6/dR2AY8CBHgHUMi7EEV9ilLUpygVClegYVBDivkWo5hPMYr6FKWYbzGK\n+xa/9XApCYLcw9Pdk1olalGrRK1MHX/5xmW2n9lORFQEEVERtyWSQtuE8nrr19M9/1zUORbvW0xB\n74L4efrh5+V32zbQLxB3N/lrUwghhEhL8sSMZwZ/cnl7Q6D9EaEij3DyubY5sDpV3QrgvSwJMpNy\nRSIBKA64A+Gp6sOB6tkfTs5wLuoceyL2oBOnJtYkbrVOsW97L6PjUh+Tet/eNkEnZHo/QSek+V5G\nZX/p/YxbPY4EnUC8jjfbhPhbr+MT4onX8dxMuJnidfJ6W4lPSPn6ZsJN4hLizDY+jriEuHS3znJX\n7vh6+uLr6YuPpw8+Hj639n09ffHx8KFcQDn8Pf3x8/LD38s/RfHz9COgQAAB3gG3toW8C+Ht4e10\nTCJvmtlz5q19rTWRMZG3kgoRURFUL2b/x+bxy8d55pdn7Caw/hn9DxUKp7+g/Xt/vMeCvQvwcvfC\n093TbN08b72uUawGIW1C7Mbx/ub3uRZ7DXc3d9yVO27K7da+u5s7Lcq1oH5Q/XTPP3/9PGuOr0Gh\nUEqhULgpt1v7Sik6VuqIj6dPum0cPH+QU1dOAdw6x7YPEFAggLtL3W33Pv769y9i42PrcxRBAAAQ\n30lEQVTNecl6k9jauKvwXZQqWCrd86/GXM1wtY8GQQ3wcvdK9/2TkSeJiIpI931/L39qFK9h9xq7\nw3cTF5/+z8CyhcranRvlWuw1Dl04ZPcadUrWsXsfYVfC7N6Hn6cf1Yvb//97T/geuz/LyxQsI/eR\nSO7DkPtIIveRRO7DcOQ+apeoLX+3OvdcG5TO8YWUUt5aaxdM8eq43JJIcJQ/wKZNm6yOI0v9df4v\n3t/3vtVhZJrtj3Bbl+rkf9wrpXBL/A8FbiT+sZ/sj/8rN67wxU9f3DrfXbnfak+ReL5KoyTWu6uk\nhxHba0/lSQFVAIXC3c0dD+Vx6zh35Y6HmwfuuOPm7oaHlwceeODhlliUB+5upg0PZeo83TxNUZ54\nunvi4eaBl/Iy7agMPr2NB66n//b1xP/OcMZF3xFrhYWFMXfuXKvDyJd2Jv5nz6zKs4hNiDU9beJj\niEmIubV/I+EGa39ai6db0kcsqb+fx84do8ClAsTreKITornK1aQkHjc573Oeuf/a//6/t/09LsVc\nupV8jCc+RTIyuGIwnct0Tvf8w1cOE7oz1O41/r+9e4+2ojzvOP79iZckJUoSFMSQGCWaWLRGI7Fe\nE7UYda1kWa8tUdG2JlFqli61JbWKt0UkCVJFYxNRImqiSaq1GsEqNd5QLNZINVoFL6CCFwIBVCTw\n9I/3PTJs9j5nzoV9Oef3WWsWZ7/vu2feOQ/7zH6feWfmyi9dyce2qD3Xctq8acx4dUbN+mFbDmPc\n7uPa3caYx8awdNXSmvWjdhjFYZ88rGZ9T+zHjfNuZPqr02vWV+5Htc9nK+5HNX11Pypj2qr7Uamv\n7ke1z2gr7kc1fXE/an0narX9qKVtPy4fcTlbf2jrdtu2usL4s9c/pkxtZ52bWZ4C8g5wVETcUSif\nCmwVEUdWtJ8MnF7XTpqZmZmZmZnBVRExprKws+PaXPcbYE5EnFUoGw1cHhENu/NES8xIiIjVkuYA\nBwN3ACjNDz0YuKLKWyblf58CVtSlk2ZmZmZmZtaX9Qd2Y914dD1dGNcCzAIqp56MzOUN0xIzEgAk\nHQtMBb7FusdkHA18LiLebGDXzMzMzMzMzDrU0bhW0nhgSESclNtvD8wlPf7xOlLSYRJweERU3oSx\nblpiRgJARNwqaSBwETAIeBI41EkEMzMzMzMzawUlxrWDgaGF9i9JOoL0lIYzgIXA3zQyiQAtNCPB\nzMzMzMzMzBpvk0Z3wMzMzMzMzMxaR8snEiR9V9LDklZKWlKlfjdJN0t6RdI7kp6WdEaNdg9IelfS\ny5LOqc8eWFFH8cxthkq6K7dZJGmCpE0q2jieTUrSZyXdLulNScskPSjpyxVtOoyxNQ9JR0h6NP+N\nXSLp3yrqHc8WI2lzSU9KWitpt4o6x7MFSPq0pGslzc+fzecljct3DC+2czxbiKTTJb2Yv988Kmmv\nRvfJOiZprKTZkv4gabGk2yTtVKXdRZJey5/Z/5Q0rBH9tc6R9I/5eDmxorxXx7M3HCg2A24FflSj\nfk9gMTAK2AW4FBgv6bS2BpI+CswAXgT2AM4Bxkn6243Yb6uu3XjmLze/Jt3fY2/gJGA06RqjtjaO\nZ3O7C+gHfJkUn98Cd0raBsrF2JqHpKOAG4ApwK7APsDNhXrHszVNIF2Dud71j45nS/kcIODvSN9/\nziTd2OvStgaOZ2uRdBzwQ+AC4Auk4+eMfK21Nbf9gSuBLwGHkL7v3iPpw20NJP0DMAY4FRgBrCTF\nd/P6d9fKysm8U0mfx2J5749nRPSKhXTwW1Ky7WTg3sLrbwNvAZsWysYDzzR6v/rqUiuepEefrAYG\nFsq+Cfy+LX6OZ/MuwCeAtcC+hbL+ueygsjH20hwLKSG0ABjdThvHs8WWHLOnSQPRtcBujmfvWICz\ngRccz9ZcgEeBfym8Finhd26j++al07EcmP++7lcoew04s/B6S+Bd4NhG99dLzTj2B54DDgL+C5jY\nl+LZG2YkdMVWQHHa/N7AAxHxx0LZDGBnSVvVtWfWkb2BuRHxVqFsBimmf1po43g2oYh4G3gWOFHS\nRyRtSkr8LAbm5GZlYmzNYQ9gCICkJ/L0vV9LKsbJ8WwhkgYBPwa+QfrCU8nxbG0D2PD7j+PZAvIl\nKXsC97WVRRqd3Av8eaP6ZV02gDTjawmApM+Q7tRfjO8fgMdwfJvZVcB/RMTMYmFfiWefSyRI2gc4\nFvjXQvFg0kCmaHGhzppHmVg5ns3tL0gD0OWkgcp3gK9GxLJc7/i1jh1IZ8QuIE2FPoJ0JvN+SQNy\nG8eztVwPXB0R/1Oj3vFsUfna3DHANYVix7N1DCTNAqsWL8eqhUgSMAl4KCKeycWDSYkFx7dFSDoe\n2B0YW6W6T8SzKRMJksbnG1bUWtZUu0FJifUOB24HxkXEfR21t56xseJpzaOTMb6a9Id0X2Av0mfy\nznwm1JpAJ+LZdgy5JCJuz4PPk0kHz2MatgO2nrLxVLoRcX/gsra3NrDbVkNXjqmStgPuBm6JiOsa\n03Mzy64m3bfk+EZ3xLpG0idJyaBREbG60f1plE0b3YEafkA6K9Ke+Z1ZoaRdSNO/romI8RXVi4DK\nQcygQp11T0/GcxFp8FlUGSvHs/5KxVjSwcDhwICIWJnLx0gaSbovxgTKxdg2rrKf2SH559+1FUbE\n+5LmA5/KRY5n45WJ54vAV0hTLlelE2Yf+G9JN0XEyTiezaBTx1RJQ4CZpLOf36xo53i2jreANVT/\nfuNYtQhJk0nfg/aPiNcLVYtIydtBrH8WexBQa4aYNc6ewNbAE1p3wOwHHCBpDOtudtur49mUiYR8\nHfXbPbW+fL3ufcD1EXF+lSazgEsk9YuINblsJPBcYbq1dVEPx3MW8F1JAwvXdI4ElgHPFNo4nnVU\nNsb57sRBusFQ0VrWnd0uE2PbiDoRzznAKmBn4JFcthmwPfBybuZ4Nlgn4vn3wD8VioaQrpc/Fpid\nyxzPBuvMMTXPRJgJPA6cUqWJ49kiImJ1/pt7MHAHfDBF/mDgikb2zcrJSYSvAwdGxCvFuoh4UdIi\nUjyfyu23JD3l4ap699U6dC/pSVVFU0knVr4XEfP7QjybMpHQGZKGAh8HPg30k/RnueqFiFiZL2eY\nSZrSN6kwfXpN4aB5M3A+cJ2ky0j/Mc4gXbttddRRPIF7SF9upuXHqmwLXAxMLkwtcjyb1yxgKXCD\npItJ90g4lTTwvCu3KRNjawIRsVzSNcCFkhaSkgfnkpJFv8jNHM8WERELi68lrSSdUZkfEa/lYsez\nReSZCPeTZpucC2zTduIsItrOkDmerWUiMDUnFGaTHun5EdIAxpqYpKuBvwK+BqwsjEeWRcR7+edJ\nwHmSXgBeIn0WFwL/XufuWgfymGS9ZGs+Zr4dEW2zNHt/PBv92IjuLqTpfWuqLAfk+gtq1M+vWM9w\n4DfAO8ArwNmN3re+uHQUz9xmKHAnsII0XegyYBPHszUW0o0W7wbeJCUVHgZGVrTpMMZemmMhTeWb\nALye4zkD+Lzj2foLKaG7hsLjHx3P1llIl4tVHkvXkk6kOJ4tugCnkQYl75KS819sdJ+8lIrb2hrf\nb0+saDeO9NjAd/LxdFij++6ldIxnUnj8Y1+Ip/JOmpmZmZmZmZl1qCmf2mBmZmZmZmZmzcmJBDMz\nMzMzMzMrzYkEMzMzMzMzMyvNiQQzMzMzMzMzK82JBDMzMzMzMzMrzYkEMzMzMzMzMyvNiQQzMzMz\nMzMzK82JBDMzMzMzMzMrzYkEMzMzMzMzMyvNiQQzM7MmIelBSRMa3Y+eJGlHSWvzMrtO27y4sM3T\n6rFNMzOzvsSJBDMzs26QdIeku2vU7Z8Hs8O7uO4FzTAQlrSFpLclnVWj/kJJCyWpxioCOAA4tBt9\n2FbSakl/WaP+p5IezS/HA4OB17u6PTMzM6vNiQQzM7PumQIcImlIlbqTgccj4n/r3KceFRGrgJtJ\n+1PNicDUiIga9QKWRMTvu9GH14HpwCkbrFzqDxwFXJvbvhMRbwBru7o9MzMzq82JBDMzs+65E3gL\nGF0slPQnwNHkwW0u+4qkxyW9J+lVSZfUOosv6UFgO+DKPKvh/Vw+UNLP8gyAlZJ+K+mYivd+NLdZ\n0TarofKyiTzLYGLuxwpJj0jav539nALsImlExbYOAT4FXN/hb2r9902T9AtJ50laLGmJpLGS+kn6\nYX79iqQTKvowUtK2Fas7njTr4eed6YOZmZl1jRMJZmZm3RARa4AbqEgkAMeSjrM/B5A0FLgLeAjY\nDTgd+BYwtsaqv0aamj+WNE1/u1z+YeAx4DBgOClRcZOkLxTeewWwF3A48FVgJLBrxfqvAfYkJTt2\nBW4DpkvavsZ+Pgk8yYYzAkYDD0TEvBr70Z6RwCeA/YBzgEtJiZlFuf/XAj+RNCi3vxNYApxUpQ+/\njIgVXeiDmZmZdZITCWZmZt13HTBM0gGFstHAryJieX59OjAvIs6MiP+LiNuBC4Gzq60wXwawFlgR\nEW9ExJu5fEFETIqIuRHxYkRcCdwHHAMgaStgFHBmRDwQEU/nvmzWtm5JnwG+ARwdEbPyer4PzGbD\nhEjRFOA4SR/K69kSODKXd8Ub+ffxfERMAeYBm0XE93Ni4tL8O9g37/sfqUjaSNoJ2KcbfTAzM7NO\nciLBzMysmyLiOeAR8tl6ScOA/Slc1gB8PrcpehjYStLgstvKU/8vkPRUvgHicuAg0uUFADsC/YDH\nC/1bCrxQWM2uuc08ScvbFtKAfMd2Nn8zsAVpFgPAXwPvA78s2/8KlfeOWAzMLfR7DWkGwjaFNtcB\nO0naL78+BXg+Ih7qYh/MzMyskzZtdAfMzMx6iSnAFZJOJ92U8IWIeHAjbGcs8G3gO8AzwErgKmDz\nTqyjPykBsHuVupqXB0TEUkm3kfbvRtLMgJ9FxHud2HbR6spN1Cj74MRHRDwraRZwsqRHSDMrruji\n9s3MzKwLPCPBzMysZ9xKmoY/CjiBDafa/450xr9oP2BpRCyqsc73STMHivYBbouIWyJiLvAS8NlC\n/TxgDekeAwBI+hgwrNDmCdKlDltHxPyK5Y32d5MpwIGSjgBGVNnPephCupTjaNJshRsa0AczM7M+\ny4kEMzOzHhARK0nJhPGkmyP+tKLJZGAHSZMk7SzpSOB84AftrPYl0qB9iKSP57LngUMl7S1pF+An\nwMBCP5aRZgtMlHSgpOGkgfdq0tl9IuLZ3NebJH1d0vaSRuSnJozsYD9nAi+TBu9zI2JOB7+ajeGW\n/O+PgLvbScSYmZnZRuBEgpmZWc+ZAgwAplcObiNiIekpCvuQnn4wmTQQ/l6xWcX6/pk022A+6UkG\nABcBTwH3APeSBvV3VLzvDNKNE+8CpgMzSfdIKF6CcAJwEzAReBb4FbAHsKDEfl6f97PsbITK/epM\nuw3KCkmbzvTBzMzMeogiyh7bzczMrBVJ6g+8CoyJiGl13vaOpFkUwyPimTpvewEwPiKurud2zczM\nejvPSDAzM+tlJO0h6ThJO0j6IulpC6vZcOZCvQQwW9L99diYpPPyUyi2rcf2zMzM+hrPSDAzM+tl\nJO0J/Jh0WcQqYA5wVr1nBOS+bMq6R1O+FxGv1WGbA4C2e0q8GRHLN/Y2zczM+hInEszMzMzMzMys\nNF/aYGZmZmZmZmalOZFgZmZmZmZmZqU5kWBmZmZmZmZmpTmRYGZmZmZmZmalOZFgZmZmZmZmZqU5\nkWBmZmZmZmZmpTmRYGZmZmZmZmalOZFgZmZmZmZmZqU5kWBmZmZmZmZmpf0/oh+SBL5sEoIAAAAA\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a1d21d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "iT = IT(nest.GetDefaults('ht_neuron'))\n",
    "\n",
    "V = np.linspace(-110, 30, 100)\n",
    "plt.plot(V, 10 * iT.tau_m(V), 'b-', label='10 * tau_m');\n",
    "plt.plot(V, iT.tau_h(V), 'b--', label='tau_h');\n",
    "ax1 = plt.gca();\n",
    "ax1.set_xlabel('Voltage V [mV]');\n",
    "ax1.set_ylabel('Time constants [ms]', color='b');\n",
    "ax2 = ax1.twinx()\n",
    "ax2.plot(V, iT.m_inf(V), 'g-', label='m_inf');\n",
    "ax2.plot(V, iT.h_inf(V), 'g--', label='h_inf');\n",
    "ax2.set_ylabel('Steady-state', color='g');\n",
    "ln1, lb1 = ax1.get_legend_handles_labels()\n",
    "ln2, lb2 = ax2.get_legend_handles_labels()\n",
    "plt.legend(ln1+ln2, lb1+lb2, loc='upper right');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Time constants here are much shorter than for I_h\n",
    "- Time constants are about five times shorter than in Fig 1 of Huguenard and McCormick, *J Neurophysiol* 68:1373, 1992, cited in [HT05], but that may be due to the fact that the original data was collected at 23-25C and parameters have been adjusted to 36C.\n",
    "- Steady-state activation and inactivation look much like in Huguenard and McCormick.\n",
    "- Note: Most detailed paper on data is Huguenard and Prince, *J Neurosci* 12:3804-3817, 1992. The parameters given for h_inf here are for VB cells, not nRT cells in that paper (Fig 5B), parameters for m_inf are similar to but not exactly those of Fig 4B for either VB or nRT."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "iT = IT(nest.GetDefaults('ht_neuron'))\n",
    "nr, cr = voltage_clamp(iT, [(200, -65.), (200, -80.), (200, -100.), (200, -90.), (200, -70.),\n",
    "                           (200, -55.)],\n",
    "                      nest_dt=0.1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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S4B2CrfGOD117urtX73hHkTpIQ/RFUpMSfBGRONq2IxjpnFm/IQ2Km7EtXwl+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\nQf64qxYNuiouLuaVV15h3LhxjBs3jr/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ONONyuWMUWfxxW3Mz5v5ZZurL7ximYZb+sDXe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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11921df50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(nr.times, nr.I_T, label='NEST');\n",
    "plt.plot(cr.times, cr.I_T, label='Control');\n",
    "plt.legend(loc='upper left');\n",
    "plt.xlabel('Time [ms]');\n",
    "plt.ylabel('I_T [mV]');\n",
    "plt.title('I_T current')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(nr.times, (nr.I_T-cr.I_T)/np.abs(cr.I_T));\n",
    "plt.title('Relative I_T error')\n",
    "plt.xlabel('Time [ms]');\n",
    "plt.ylabel('Rel. error (NEST-Control)/|Control|');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Also here the results are in good agreement and the error appears acceptable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### I_NaP channel\n",
    "\n",
    "This channel adapts instantaneously to changes in membrane potential:\n",
    "\n",
    "\\begin{align}\n",
    "I_{NaP} &= - g_{\\text{peak}, NaP} (m_{NaP}^{\\infty}(V, t))^3 (V-E_{NaP}) \\\\\n",
    "m_{NaP}^{\\infty}(V) &= \\frac{1}{1+\\exp\\left(-\\frac{V+55.7\\text{mV}}{7.7\\text{mV}}\\right)}\n",
    "\\end{align}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nest.ResetKernel()\n",
    "class INaP(Channel):\n",
    "    \n",
    "    nest_g = 'g_peak_NaP'\n",
    "    nest_I = 'I_NaP'\n",
    "    \n",
    "    def __init__(self, ht_params):\n",
    "        self.hp = ht_params\n",
    "        \n",
    "    def m_inf(self, V):\n",
    "        return 1/(1+np.exp(-(V+55.7)/7.7))\n",
    "    \n",
    "    def compute_I(self, t, V, m0, h0, D0):\n",
    "        return self.I_V_curve(V * np.ones_like(t)) \n",
    "\n",
    "    def I_V_curve(self, V):\n",
    "        self.m = self.m_inf(V)\n",
    "        return - self.hp['g_peak_NaP'] * self.m**3 * (V - self.hp['E_rev_NaP'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "iNaP = INaP(nest.GetDefaults('ht_neuron'))\n",
    "V = np.arange(-110., 30., 1.)\n",
    "nr, cr = voltage_clamp(iNaP, [(1, v) for v in V], nest_dt=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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PPZcjjjhi0zFz5szh+uuvp3///hx00EGbytu0aRPLSxUREREREam1lLzXcp06deKRRx7h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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1196f62d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(nr.times, nr.I_NaP, label='NEST');\n",
    "plt.plot(cr.times, cr.I_NaP, label='Control');\n",
    "plt.legend(loc='upper left');\n",
    "plt.xlabel('Time [ms]');\n",
    "plt.ylabel('I_NaP [mV]');\n",
    "plt.title('I_NaP current')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(nr.times, (nr.I_NaP-cr.I_NaP));\n",
    "plt.title('I_NaP error')\n",
    "plt.xlabel('Time [ms]');\n",
    "plt.ylabel('Error (NEST-Control)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Perfect agreement\n",
    "- Step structure is because $V$ changes only every second."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### I_KNa channel (aka I_DK)\n",
    "\n",
    "Equations for this channel are\n",
    "\n",
    "\\begin{align}\n",
    "I_{DK} &= - g_{\\text{peak},DK} m_{DK}(V,t) (V - E_{DK})\\\\\n",
    " m_{DK} &= \\frac{1}{1 + \\left(\\frac{d_{1/2}}{D}\\right)^{3.5}}\\\\\n",
    " \\frac{dD}{dt} &= D_{\\text{influx}}(V) - \\frac{D-D_{\\text{eq}}}{\\tau_D} = \\frac{D_{\\infty}(V)-D}{\\tau_D} \\\\\n",
    " D_{\\infty}(V) &= \\tau_D D_{\\text{influx}}(V) + {D_{\\text{eq}}}\\\\\n",
    " D_{\\text{influx}} &= \\frac{D_{\\text{influx,peak}}}{1+ \\exp\\left(-\\frac{V-D_{\\theta}}{\\sigma_D}\\right)} \n",
    "\\end{align}\n",
    "\n",
    "with \n",
    "\n",
    "|$D_{\\text{influx,peak}}$|$D_{\\text{eq}}$|$\\tau_D$|$D_{\\theta}$|$\\sigma_D$|$d_{1/2}$|\n",
    "| --: | --: | --: | --: | --: | --: |\n",
    "|$0.025\\text{ms}^{-1}$ |$0.001$|$1250\\text{ms}$|$-10\\text{mV}$|$5\\text{mV}$|$0.25$|\n",
    "\n",
    "Note the following:\n",
    "- $D_{eq}$ is the equilibrium value only for $D_{\\text{influx}}(V)=0$, i.e., in the limit $V\\to -\\infty$ and $t\\to\\infty$.\n",
    "- The actual steady-state value is $D_{\\infty}$.\n",
    "- $m_{DK}$ is a steep sigmoid which is almost 0 or 1 except for a narrow window around $d_{1/2}$.\n",
    "- To the left of this window, $I_{DK}\\approx 0$.\n",
    "- To the right of this window, $I_{DK}\\sim -(V-E_{DK})$.\n",
    "- $m_{DK}$ is not integrated over time, instead it is an instantaneous transform of $D$, which is integrated over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nest.ResetKernel()\n",
    "class IDK(Channel):\n",
    "    \n",
    "    nest_g = 'g_peak_KNa'\n",
    "    nest_I = 'I_KNa'\n",
    "    \n",
    "    def __init__(self, ht_params):\n",
    "        self.hp = ht_params\n",
    "        \n",
    "    def m_DK(self, D):\n",
    "        return 1/(1+(0.25/D)**3.5)\n",
    "\n",
    "    def D_inf(self, V):\n",
    "        return 1250. * self.D_influx(V) + 0.001\n",
    "    \n",
    "    def D_influx(self, V):\n",
    "        return 0.025 / ( 1 + np.exp(-(V+10)/5.) )\n",
    "    \n",
    "    def dD(self, D, t, V):\n",
    "        return (self.D_inf(V) - D)/1250.\n",
    "    \n",
    "    def compute_I(self, t, V, m0, h0, D0):\n",
    "        self.D = si.odeint(self.dD, D0, t, args=(V,))\n",
    "        self.m = self.m_DK(self.D)\n",
    "        return - self.hp['g_peak_KNa'] * self.m * (V - self.hp['E_rev_KNa'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Properties of I_DK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "iDK = IDK(nest.GetDefaults('ht_neuron'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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Qhb53njez7wjn4jnANdHsupzvuxDOKf9LM+9TNjzXr3D3eSnTsj3X9zKzR8xsISF5P4dQ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\nZcAtFlpWnFAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a6469d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "D=np.linspace(0.01, 1.5,num=200);\n",
    "V=np.linspace(-110, 30, num=200);\n",
    "\n",
    "ax1 = plt.subplot2grid((1, 9), (0, 0), colspan=4);\n",
    "ax2 = ax1.twinx()\n",
    "ax3 = plt.subplot2grid((1, 9), (0, 6), colspan=3);\n",
    "\n",
    "ax1.plot(V, -iDK.m_DK(iDK.D_inf(V))*(V - iDK.hp['E_rev_KNa']), 'g');\n",
    "ax1.set_ylabel('Current I_inf(V)', color='g');\n",
    "ax2.plot(V, iDK.m_DK(iDK.D_inf(V)), 'b');\n",
    "ax2.set_ylabel('Activation m_inf(D_inf(V))', color='b');\n",
    "ax1.set_xlabel('Membrane potential V [mV]');\n",
    "ax2.set_title('Steady-state activation and current');\n",
    "\n",
    "ax3.plot(D, iDK.m_DK(D), 'b');\n",
    "ax3.set_xlabel('D');\n",
    "ax3.set_ylabel('Activation m_inf(D)', color='b');\n",
    "ax3.set_title('Activation as function of D');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Note that current in steady state is \n",
    "    - $\\approx 0$ for $V < -40$mV\n",
    "    - $\\sim -(V-E_{DK})$ for $V> -30$mV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Voltage clamp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nr, cr = voltage_clamp(iDK, [(500, -65.), (500, -35.), (500, -25.), (500, 0.), (5000, -70.)],\n",
    "                      nest_dt=1.) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AH1g7kDeXL8g6FDMrceura7n6rse55tF/8b+1/6J6wMuwfiDb6jBO2OVLnHTw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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115a33550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax1 = plt.subplot2grid((1, 9), (0, 0), colspan=4);\n",
    "ax2 = plt.subplot2grid((1, 9), (0, 6), colspan=3);\n",
    "\n",
    "ax1.plot(nr.times, nr.I_KNa, label='NEST');\n",
    "ax1.plot(cr.times, cr.I_KNa, label='Control');\n",
    "ax1.legend(loc='lower right');\n",
    "ax1.set_xlabel('Time [ms]');\n",
    "ax1.set_ylabel('I_DK [mV]');\n",
    "ax1.set_title('I_DK current');\n",
    "\n",
    "ax2.plot(nr.times, (nr.I_KNa-cr.I_KNa)/np.abs(cr.I_KNa));\n",
    "ax2.set_title('Relative I_DK error')\n",
    "ax2.set_xlabel('Time [ms]');\n",
    "ax2.set_ylabel('Rel. error (NEST-Control)/|Control|');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Looks very fine.\n",
    "- Note that the current gets appreviable only when $V>-35$ mV\n",
    "- Once that threshold is crossed, the current adjust instantaneously to changes in $V$, since it is in the linear regime.\n",
    "- When returning from $V=0$ to $V=-70$ mV, the current remains large for a long time since $D$ has to drop below 1 before $m_{\\infty}$ changes appreciably"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Synaptic channels\n",
    "\n",
    "For synaptic channels, NEST allows recording of conductances, so we test conductances directly. Due to the voltage-dependence of the NMDA channels, we still do this in voltage clamp."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nest.ResetKernel()\n",
    "class SynChannel:\n",
    "    \"\"\"\n",
    "    Base class for synapse channel models in Python.\n",
    "    \"\"\"\n",
    "\n",
    "    def t_peak(self):\n",
    "        return self.tau_1 * self.tau_2 / (self.tau_2 - self.tau_1) * np.log(self.tau_2/self.tau_1)\n",
    "    \n",
    "    def beta(self, t):\n",
    "        val = ( ( np.exp(-t/self.tau_1) - np.exp(-t/self.tau_2) ) /\n",
    "                ( np.exp(-self.t_peak()/self.tau_1) - np.exp(-self.t_peak()/self.tau_2) ) )\n",
    "        val[t < 0] = 0\n",
    "        return val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def syn_voltage_clamp(channel, DT_V_seq, nest_dt=0.1):\n",
    "    \"Run voltage clamp with voltage V through intervals DT with single spike at time 1\"\n",
    "\n",
    "    spike_time = 1.0\n",
    "    delay = 1.0\n",
    "    \n",
    "    nest.ResetKernel()\n",
    "    nest.SetKernelStatus({'resolution': nest_dt})\n",
    "    try:\n",
    "        nrn = nest.Create('ht_neuron', params={'theta': 1e6, 'theta_eq': 1e6,\n",
    "                                               'instant_unblock_NMDA': channel.instantaneous})\n",
    "    except:\n",
    "        nrn = nest.Create('ht_neuron', params={'theta': 1e6, 'theta_eq': 1e6})\n",
    "\n",
    "    mm = nest.Create('multimeter', \n",
    "                     params={'record_from': ['g_'+channel.receptor],\n",
    "                             'interval': nest_dt})\n",
    "    sg = nest.Create('spike_generator', params={'spike_times': [spike_time]})\n",
    "    nest.Connect(mm, nrn)\n",
    "    nest.Connect(sg, nrn, syn_spec={'weight': 1.0, 'delay': delay,\n",
    "                                    'receptor_type': channel.rec_code})\n",
    "\n",
    "    # ensure we start from equilibrated state\n",
    "    nest.SetStatus(nrn, {'V_m': DT_V_seq[0][1], 'equilibrate': True,\n",
    "                         'voltage_clamp': True})\n",
    "    for DT, V in DT_V_seq:\n",
    "        nest.SetStatus(nrn, {'V_m': V, 'voltage_clamp': True})\n",
    "        nest.Simulate(DT)\n",
    "    t_end = nest.GetKernelStatus()['time']\n",
    "    \n",
    "    # simulate a little more so we get all data up to t_end to multimeter\n",
    "    nest.Simulate(2 * nest.GetKernelStatus()['min_delay'])\n",
    "    \n",
    "    tmp = pd.DataFrame(nest.GetStatus(mm)[0]['events'])\n",
    "    nest_res = tmp[tmp.times <= t_end]\n",
    "    \n",
    "    # Control part\n",
    "    t_old = 0.\n",
    "    t_all, g_all = [], []\n",
    "        \n",
    "    m_fast_old = (channel.m_inf(DT_V_seq[0][1]) \n",
    "                 if channel.receptor == 'NMDA' and not channel.instantaneous else None)    \n",
    "    m_slow_old = (channel.m_inf(DT_V_seq[0][1]) \n",
    "                 if channel.receptor == 'NMDA' and not channel.instantaneous else None)    \n",
    "\n",
    "    for DT, V in DT_V_seq:\n",
    "        t_loc = np.arange(0., DT+0.1*nest_dt, nest_dt)\n",
    "        g_loc = channel.g(t_old+t_loc-(spike_time+delay), V, m_fast_old, m_slow_old)\n",
    "        t_all.extend(t_old + t_loc[1:])\n",
    "        g_all.extend(g_loc[1:])\n",
    "        m_fast_old = channel.m_fast[-1] if m_fast_old is not None else None\n",
    "        m_slow_old = channel.m_slow[-1] if m_slow_old is not None else None\n",
    "        t_old = t_all[-1]\n",
    "        \n",
    "    ctrl_res = pd.DataFrame({'times': t_all, 'g_'+channel.receptor: g_all})\n",
    "\n",
    "    return nest_res, ctrl_res"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### AMPA, GABA_A, GABA_B channels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nest.ResetKernel()\n",
    "class PlainChannel(SynChannel):\n",
    "    def __init__(self, hp, receptor):\n",
    "        self.hp = hp\n",
    "        self.receptor = receptor\n",
    "        self.rec_code = hp['receptor_types'][receptor]\n",
    "        self.tau_1 = hp['tau_rise_'+receptor]\n",
    "        self.tau_2 = hp['tau_decay_'+receptor]\n",
    "        self.g_peak = hp['g_peak_'+receptor]\n",
    "        self.E_rev = hp['E_rev_'+receptor]\n",
    "        \n",
    "    def g(self, t, V, mf0, ms0):\n",
    "        return self.g_peak * self.beta(t)\n",
    "    \n",
    "    def I(self, t, V):\n",
    "        return - self.g(t) * (V-self.E_rev)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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DlxA8bx+vOSvqNCiTJ9UdNAjeeivqKEREJBVSPaluRjyDL82zvHIVVr01Bfm5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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a0b4750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ampa = PlainChannel(nest.GetDefaults('ht_neuron'), 'AMPA')\n",
    "am_n, am_c = syn_voltage_clamp(ampa, [(25, -70.)], nest_dt=0.1)\n",
    "plt.subplot(1, 2, 1);\n",
    "plt.plot(am_n.times, am_n.g_AMPA, label='NEST');\n",
    "plt.plot(am_c.times, am_c.g_AMPA, label='Control');\n",
    "plt.xlabel('Time [ms]');\n",
    "plt.ylabel('g_AMPA');\n",
    "plt.title('AMPA Channel');\n",
    "plt.subplot(1, 2, 2);\n",
    "plt.plot(am_n.times, (am_n.g_AMPA-am_c.g_AMPA)/am_c.g_AMPA);\n",
    "plt.xlabel('Time [ms]');\n",
    "plt.ylabel('Rel error');\n",
    "plt.title('AMPA rel error');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Looks quite good, but the error is maybe a bit larger than one would hope.\n",
    "- But the synaptic rise time is short (0.5 ms) compared to the integration step in NEST (0.1 ms), which may explain the error.\n",
    "- Reducing the time step reduces the error:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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jFRERyUZK8HNErVfQ3+JvwQc4cuxEfMBy7nrq1bhDERGR/DIVuMbdb3L3t4Dv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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a6c3710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ampa = PlainChannel(nest.GetDefaults('ht_neuron'), 'AMPA')\n",
    "am_n, am_c = syn_voltage_clamp(ampa, [(25, -70.)], nest_dt=0.001)\n",
    "plt.subplot(1, 2, 1);\n",
    "plt.plot(am_n.times, am_n.g_AMPA, label='NEST');\n",
    "plt.plot(am_c.times, am_c.g_AMPA, label='Control');\n",
    "plt.xlabel('Time [ms]');\n",
    "plt.ylabel('g_AMPA');\n",
    "plt.title('AMPA Channel');\n",
    "plt.subplot(1, 2, 2);\n",
    "plt.plot(am_n.times, (am_n.g_AMPA-am_c.g_AMPA)/am_c.g_AMPA);\n",
    "plt.xlabel('Time [ms]');\n",
    "plt.ylabel('Rel error');\n",
    "plt.title('AMPA rel error');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5QAl+Fs1esBiaLqNL6bqxxXDCdsNYXjqZB199L7YYRERECk3PnlBWBgsXxh2J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\nRERERERE2gEV+CIiIiIiIiLtwP8DD5gCXO7kwxYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119066d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gaba_a = PlainChannel(nest.GetDefaults('ht_neuron'), 'GABA_A')\n",
    "ga_n, ga_c = syn_voltage_clamp(gaba_a, [(50, -70.)])\n",
    "plt.subplot(1, 2, 1);\n",
    "plt.plot(ga_n.times, ga_n.g_GABA_A, label='NEST');\n",
    "plt.plot(ga_c.times, ga_c.g_GABA_A, label='Control');\n",
    "plt.xlabel('Time [ms]');\n",
    "plt.ylabel('g_GABA_A');\n",
    "plt.title('GABA_A Channel');\n",
    "plt.subplot(1, 2, 2);\n",
    "plt.plot(ga_n.times, (ga_n.g_GABA_A-ga_c.g_GABA_A)/ga_c.g_GABA_A);\n",
    "plt.xlabel('Time [ms]');\n",
    "plt.ylabel('Rel error');\n",
    "plt.title('GABA_A rel error');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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14PARkLuSiw45POpQst6Qqy+j6aKzeGL2uTzx7tdRhyMiIiIiIik89RSYwSGH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\nEBEREREREamgVBQQERERERERqaD+H6Msb4rMGk6xAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a7c2490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gaba_b = PlainChannel(nest.GetDefaults('ht_neuron'), 'GABA_B')\n",
    "gb_n, gb_c = syn_voltage_clamp(gaba_b, [(750, -70.)])\n",
    "plt.subplot(1, 2, 1);\n",
    "plt.plot(gb_n.times, gb_n.g_GABA_B, label='NEST');\n",
    "plt.plot(gb_c.times, gb_c.g_GABA_B, label='Control');\n",
    "plt.xlabel('Time [ms]');\n",
    "plt.ylabel('g_GABA_B');\n",
    "plt.title('GABA_B Channel');\n",
    "plt.subplot(1, 2, 2);\n",
    "plt.plot(gb_n.times, (gb_n.g_GABA_B-gb_c.g_GABA_B)/gb_c.g_GABA_B);\n",
    "plt.xlabel('Time [ms]');\n",
    "plt.ylabel('Rel error');\n",
    "plt.title('GABA_B rel error');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Looks good for all\n",
    "- For GABA_B the error is negligible even for dt = 0.1, since the time constants are large."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### NMDA Channel\n",
    "\n",
    "The equations for this channel are\n",
    "\n",
    "\\begin{align}\n",
    "    \\bar{g}_{\\text{NMDA}}(t) &= m(V, t) g_{\\text{NMDA}}(t)     m(V, t)\\\\ &= a(V) m_{\\text{fast}}^*(V, t) + ( 1 - a(V) ) m_{\\text{slow}}^*(V, t)\\\\\n",
    "     a(V)    &= 0.51 - 0.0028 V \\\\\n",
    "     m^{\\infty}(V) &= \\frac{1}{ 1 + \\exp\\left( -S_{\\text{act}} ( V - V_{\\text{act}} ) \\right) } \\\\\n",
    "     m_X^*(V, t) &= \\min(m^{\\infty}(V), m_X(V, t))\\\\\n",
    "      \\frac{\\text{d}m_X}{\\text{d}t} &= \\frac{m^{\\infty}(V) - m_X }{ \\tau_{\\text{Mg}, X}}\n",
    "\\end{align} \n",
    "\n",
    "where $g_{\\text{NMDA}}(t)$ is the beta functions as for the other channels. In case of instantaneous unblocking, $m=m^{\\infty}$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### NMDA with instantaneous unblocking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class NMDAInstantChannel(SynChannel):\n",
    "    def __init__(self, hp, receptor):\n",
    "        self.hp = hp\n",
    "        self.receptor = receptor\n",
    "        self.rec_code = hp['receptor_types'][receptor]\n",
    "        self.tau_1 = hp['tau_rise_'+receptor]\n",
    "        self.tau_2 = hp['tau_decay_'+receptor]\n",
    "        self.g_peak = hp['g_peak_'+receptor]\n",
    "        self.E_rev = hp['E_rev_'+receptor]\n",
    "        self.S_act = hp['S_act_NMDA']\n",
    "        self.V_act = hp['V_act_NMDA']\n",
    "        self.instantaneous = True\n",
    "        \n",
    "    def m_inf(self, V):\n",
    "        return 1. / ( 1. + np.exp(-self.S_act*(V-self.V_act)))\n",
    "    \n",
    "    def g(self, t, V, mf0, ms0):\n",
    "        return self.g_peak * self.m_inf(V) * self.beta(t)\n",
    "    \n",
    "    def I(self, t, V):\n",
    "        return - self.g(t) * (V-self.E_rev)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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KZtYkbfdEwrCH9HtxTffG3oR2Qr7GEO6pZ5pZ8wxx5jRnjYdlEl8Bjs/0meZaX4b6s7rn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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118b71f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nmdai = NMDAInstantChannel(nest.GetDefaults('ht_neuron'), 'NMDA')\n",
    "ni_n, ni_c = syn_voltage_clamp(nmdai, [(50, -60.), (50, -50.), (50, -20.), (50, 0.), (50, -60.)])\n",
    "plt.subplot(1, 2, 1);\n",
    "plt.plot(ni_n.times, ni_n.g_NMDA, label='NEST');\n",
    "plt.plot(ni_c.times, ni_c.g_NMDA, label='Control');\n",
    "plt.xlabel('Time [ms]');\n",
    "plt.ylabel('g_NMDA');\n",
    "plt.title('NMDA Channel (instant unblock)');\n",
    "plt.subplot(1, 2, 2);\n",
    "plt.plot(ni_n.times, (ni_n.g_NMDA-ni_c.g_NMDA)/ni_c.g_NMDA);\n",
    "plt.xlabel('Time [ms]');\n",
    "plt.ylabel('Rel error');\n",
    "plt.title('NMDA (inst) rel error');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Looks good\n",
    "- Jumps are due to blocking/unblocking of Mg channels with changes in $V$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### NMDA with unblocking over time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class NMDAChannel(SynChannel):\n",
    "    def __init__(self, hp, receptor):\n",
    "        self.hp = hp\n",
    "        self.receptor = receptor\n",
    "        self.rec_code = hp['receptor_types'][receptor]\n",
    "        self.tau_1 = hp['tau_rise_'+receptor]\n",
    "        self.tau_2 = hp['tau_decay_'+receptor]\n",
    "        self.g_peak = hp['g_peak_'+receptor]\n",
    "        self.E_rev = hp['E_rev_'+receptor]\n",
    "        self.S_act = hp['S_act_NMDA']\n",
    "        self.V_act = hp['V_act_NMDA']\n",
    "        self.tau_fast = hp['tau_Mg_fast_NMDA']\n",
    "        self.tau_slow = hp['tau_Mg_slow_NMDA']\n",
    "        self.instantaneous = False\n",
    "        \n",
    "    def m_inf(self, V):\n",
    "        return 1. / ( 1. + np.exp(-self.S_act*(V-self.V_act)) )\n",
    "    \n",
    "    def dm(self, m, t, V, tau):\n",
    "        return ( self.m_inf(V) - m ) / tau\n",
    "\n",
    "    def g(self, t, V, mf0, ms0):\n",
    "        self.m_fast = si.odeint(self.dm, mf0, t, args=(V, self.tau_fast))\n",
    "        self.m_slow = si.odeint(self.dm, ms0, t, args=(V, self.tau_slow))\n",
    "        a = 0.51 - 0.0028 * V\n",
    "        m_inf = self.m_inf(V)\n",
    "        mfs = self.m_fast[:]\n",
    "        mfs[mfs > m_inf] = m_inf\n",
    "        mss = self.m_slow[:]\n",
    "        mss[mss > m_inf] = m_inf\n",
    "        m = np.squeeze(a * mfs + ( 1 - a ) * mss)\n",
    "        return self.g_peak * m * self.beta(t)\n",
    "    \n",
    "    def I(self, t, V):\n",
    "        raise NotImplementedError()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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2nV9fRESyjxIGIiLZLfLEQWgkcKGZ9TaznYAHCIoUPgZgZsPM7PG49g8A25jZ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\n6f8BWQ3ACsFCoDIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11979e150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nmda = NMDAChannel(nest.GetDefaults('ht_neuron'), 'NMDA')\n",
    "nm_n, nm_c = syn_voltage_clamp(nmda, [(50, -70.), (50, -50.), (50, -20.), (50, 0.), (50, -60.)])\n",
    "plt.subplot(1, 2, 1);\n",
    "plt.plot(nm_n.times, nm_n.g_NMDA, label='NEST');\n",
    "plt.plot(nm_c.times, nm_c.g_NMDA, label='Control');\n",
    "plt.xlabel('Time [ms]');\n",
    "plt.ylabel('g_NMDA');\n",
    "plt.title('NMDA Channel');\n",
    "plt.subplot(1, 2, 2);\n",
    "plt.plot(nm_n.times, (nm_n.g_NMDA-nm_c.g_NMDA)/nm_c.g_NMDA);\n",
    "plt.xlabel('Time [ms]');\n",
    "plt.ylabel('Rel error');\n",
    "plt.title('NMDA rel error');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Looks fine, too."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Synapse Model\n",
    "\n",
    "We test the synapse model by placing it between two parrot neurons, sending spikes with differing intervals and compare to expected weights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Recorded weights:', array([ 1.        ,  0.875499  ,  0.76977486,  0.67387928,  0.64996814,\n",
      "        0.64689954,  0.9123844 ]))\n",
      "('Expected weights:', (1.0, 0.8754990013320011, 0.7697748551001631, 0.6738792820453234, 0.6499681432540876, 0.6468995408997453, 0.9123844012053444))\n",
      "('Difference      :', array([ 0.,  0.,  0.,  0.,  0.,  0.,  0.]))\n"
     ]
    }
   ],
   "source": [
    "nest.ResetKernel()\n",
    "sp = nest.GetDefaults('ht_synapse')\n",
    "P0 = sp['P']\n",
    "dP = sp['delta_P']\n",
    "tP = sp['tau_P']\n",
    "spike_times = [10., 12., 20., 20.5, 100., 200., 1000.]\n",
    "expected = [(0., P0, P0)]\n",
    "for idx, t in enumerate(spike_times):\n",
    "    tlast, Psend, Ppost = expected[idx]\n",
    "    Psend = 1 - (1-Ppost)*math.exp(-(t-tlast)/tP)\n",
    "    expected.append((t, Psend, (1-dP)*Psend))\n",
    "expected_weights = list(zip(*expected[1:]))[1]\n",
    "\n",
    "sg = nest.Create('spike_generator', params={'spike_times': spike_times})\n",
    "n = nest.Create('parrot_neuron', 2)\n",
    "wr = nest.Create('weight_recorder')\n",
    "\n",
    "nest.SetDefaults('ht_synapse', {'weight_recorder': wr[0], 'weight': 1.0})\n",
    "nest.Connect(sg, n[:1])\n",
    "nest.Connect(n[:1], n[1:], syn_spec='ht_synapse')\n",
    "nest.Simulate(1200)\n",
    "\n",
    "rec_weights = nest.GetStatus(wr)[0]['events']['weights']\n",
    "\n",
    "print('Recorded weights:', rec_weights)\n",
    "print('Expected weights:', expected_weights)\n",
    "print('Difference      :', np.array(rec_weights) - np.array(expected_weights))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perfect agreement, synapse model looks fine."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Integration test: Neuron driven through all synapses\n",
    "\n",
    "We drive a Hill-Tononi neuron through pulse packets arriving at 1 second intervals, impinging through all synapse types. Compare this to Fig 5 of [HT05]."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nest.ResetKernel()\n",
    "nrn = nest.Create('ht_neuron')\n",
    "ppg = nest.Create('pulsepacket_generator', n=4,\n",
    "                  params={'pulse_times': [700., 1700., 2700., 3700.],\n",
    "                          'activity': 700, 'sdev': 50.})\n",
    "pr = nest.Create('parrot_neuron', n=4)\n",
    "mm = nest.Create('multimeter', \n",
    "                 params={'interval': 0.1,\n",
    "                         'record_from': ['V_m', 'theta',\n",
    "                                         'g_AMPA', 'g_NMDA',\n",
    "                                         'g_GABA_A', 'g_GABA_B',\n",
    "                                         'I_NaP', 'I_KNa', 'I_T', 'I_h']})\n",
    "\n",
    "weights = {'AMPA': 25., 'NMDA': 20., 'GABA_A': 10., 'GABA_B': 1.}\n",
    "receptors = nest.GetDefaults('ht_neuron')['receptor_types']\n",
    "\n",
    "nest.Connect(ppg, pr, 'one_to_one')\n",
    "for p, (rec_name, rec_wgt) in zip(pr, weights.items()):\n",
    "    nest.Connect([p], nrn, syn_spec={'model': 'ht_synapse',\n",
    "                                       'receptor_type': receptors[rec_name],\n",
    "                                       'weight': rec_wgt})\n",
    "nest.Connect(mm, nrn)\n",
    "\n",
    "nest.Simulate(5000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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XrtwMn53xWGhuh98Cu5lZse8v0b4fzLcS9U/859wvWKfodxGU/bfAp81fsSD6esNDt4tu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\nudQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1197a52d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = nest.GetStatus(mm)[0]['events']\n",
    "t = data['times']\n",
    "def texify_name(name):\n",
    "    return r'${}_{{\\mathrm{{{}}}}}$'.format(*name.split('_'))\n",
    "\n",
    "fig = plt.figure(figsize=(12,10))\n",
    "\n",
    "Vax = fig.add_subplot(311)\n",
    "Vax.plot(t, data['V_m'], 'k', lw=1, label=r'$V_m$')\n",
    "Vax.plot(t, data['theta'], 'r', alpha=0.5, lw=1, label=r'$\\Theta$')\n",
    "Vax.set_ylabel('Potential [mV]')\n",
    "Vax.legend(fontsize='small')\n",
    "Vax.set_title('ht_neuron driven by sinousiodal Poisson processes')\n",
    "\n",
    "Iax = fig.add_subplot(312)\n",
    "for iname, color in (('I_h', 'blue'), ('I_KNa', 'green'),\n",
    "                     ('I_NaP', 'red'), ('I_T', 'cyan')):\n",
    "    Iax.plot(t, data[iname], color=color, lw=1, label=texify_name(iname))\n",
    "#Iax.set_ylim(-60, 60)\n",
    "Iax.legend(fontsize='small')\n",
    "Iax.set_ylabel('Current [mV]')\n",
    "\n",
    "Gax = fig.add_subplot(313)\n",
    "for gname, sgn, color in (('g_AMPA', 1, 'green'), ('g_GABA_A', -1, 'red'), \n",
    "                          ('g_GABA_B', -1, 'cyan'), ('g_NMDA', 1, 'magenta')):\n",
    "    Gax.plot(t, sgn*data[gname], lw=1, label=texify_name(gname), color=color)\n",
    "#Gax.set_ylim(-150, 150)\n",
    "Gax.legend(fontsize='small')\n",
    "Gax.set_ylabel('Conductance')\n",
    "Gax.set_xlabel('Time [ms]');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda env:py27]",
   "language": "python",
   "name": "conda-env-py27-py"
  },
  "language_info": {
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